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<h1 class="ltx_title ltx_title_document">The Prompt Report: A Systematic Survey of Prompting Techniques</h1>
<div class="ltx_authors">
<span class="ltx_creator ltx_role_author">
<span class="ltx_personname">Sander Schulhoff<sup id="id44.44.id1" class="ltx_sup"><span id="id44.44.id1.1" class="ltx_text ltx_font_italic">1∗</span></sup>   
Michael Ilie<sup id="id45.45.id2" class="ltx_sup"><span id="id45.45.id2.1" class="ltx_text ltx_font_italic">1∗</span></sup>
<span id="id3.3.1" class="ltx_text ltx_font_bold">Nishant Balepur<sup id="id3.3.1.1" class="ltx_sup"><span id="id3.3.1.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id4.4.2" class="ltx_text ltx_font_bold">Konstantine Kahadze<sup id="id4.4.2.1" class="ltx_sup"><span id="id4.4.2.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  

<br class="ltx_break"><span id="id5.5.3" class="ltx_text ltx_font_bold">Amanda Liu<sup id="id5.5.3.1" class="ltx_sup"><span id="id5.5.3.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id6.6.4" class="ltx_text ltx_font_bold">Chenglei Si<sup id="id6.6.4.1" class="ltx_sup"><span id="id6.6.4.1.1" class="ltx_text ltx_font_medium ltx_font_italic">3</span></sup></span>  
<span id="id7.7.5" class="ltx_text ltx_font_bold">Yinheng Li<sup id="id7.7.5.1" class="ltx_sup"><span id="id7.7.5.1.1" class="ltx_text ltx_font_medium ltx_font_italic">4</span></sup></span>  
<span id="id8.8.6" class="ltx_text ltx_font_bold">Aayush Gupta<sup id="id8.8.6.1" class="ltx_sup"><span id="id8.8.6.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id9.9.7" class="ltx_text ltx_font_bold">HyoJung Han<sup id="id9.9.7.1" class="ltx_sup"><span id="id9.9.7.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id10.10.8" class="ltx_text ltx_font_bold">Sevien Schulhoff<sup id="id10.10.8.1" class="ltx_sup"><span id="id10.10.8.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  

<br class="ltx_break"><span id="id11.11.9" class="ltx_text ltx_font_bold">Pranav Sandeep Dulepet<sup id="id11.11.9.1" class="ltx_sup"><span id="id11.11.9.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id12.12.10" class="ltx_text ltx_font_bold">Saurav Vidyadhara<sup id="id12.12.10.1" class="ltx_sup"><span id="id12.12.10.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id13.13.11" class="ltx_text ltx_font_bold">Dayeon Ki<sup id="id13.13.11.1" class="ltx_sup"><span id="id13.13.11.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id14.14.12" class="ltx_text ltx_font_bold">Sweta Agrawal<sup id="id14.14.12.1" class="ltx_sup"><span id="id14.14.12.1.1" class="ltx_text ltx_font_medium ltx_font_italic">11</span></sup></span>  
<span id="id15.15.13" class="ltx_text ltx_font_bold">Chau Pham<sup id="id15.15.13.1" class="ltx_sup"><span id="id15.15.13.1.1" class="ltx_text ltx_font_medium ltx_font_italic">12</span></sup></span>  

<br class="ltx_break"><span id="id16.16.14" class="ltx_text ltx_font_bold">Gerson Kroiz</span>  
<span id="id17.17.15" class="ltx_text ltx_font_bold">Feileen Li<sup id="id17.17.15.1" class="ltx_sup"><span id="id17.17.15.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id18.18.16" class="ltx_text ltx_font_bold">Hudson Tao<sup id="id18.18.16.1" class="ltx_sup"><span id="id18.18.16.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id19.19.17" class="ltx_text ltx_font_bold">Ashay Srivastava<sup id="id19.19.17.1" class="ltx_sup"><span id="id19.19.17.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id20.20.18" class="ltx_text ltx_font_bold">Hevander Da Costa<sup id="id20.20.18.1" class="ltx_sup"><span id="id20.20.18.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id21.21.19" class="ltx_text ltx_font_bold">Saloni Gupta<sup id="id21.21.19.1" class="ltx_sup"><span id="id21.21.19.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  

<br class="ltx_break"><span id="id22.22.20" class="ltx_text ltx_font_bold">Megan L. Rogers<sup id="id22.22.20.1" class="ltx_sup"><span id="id22.22.20.1.1" class="ltx_text ltx_font_medium ltx_font_italic">7</span></sup></span>  
<span id="id23.23.21" class="ltx_text ltx_font_bold">Inna Goncearenco<sup id="id23.23.21.1" class="ltx_sup"><span id="id23.23.21.1.1" class="ltx_text ltx_font_medium ltx_font_italic">8</span></sup></span>  
<span id="id24.24.22" class="ltx_text ltx_font_bold">Giuseppe Sarli<sup id="id24.24.22.1" class="ltx_sup"><span id="id24.24.22.1.1" class="ltx_text ltx_font_medium ltx_font_italic">8,9</span></sup></span>  
<span id="id25.25.23" class="ltx_text ltx_font_bold">Igor Galynker<sup id="id25.25.23.1" class="ltx_sup"><span id="id25.25.23.1.1" class="ltx_text ltx_font_medium ltx_font_italic">10</span></sup></span>  

<br class="ltx_break"><span id="id26.26.24" class="ltx_text ltx_font_bold">Denis Peskoff<sup id="id26.26.24.1" class="ltx_sup"><span id="id26.26.24.1.1" class="ltx_text ltx_font_medium ltx_font_italic">6</span></sup></span>
 
<span id="id27.27.25" class="ltx_text ltx_font_bold">Marine Carpuat<sup id="id27.27.25.1" class="ltx_sup"><span id="id27.27.25.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>
 
<span id="id28.28.26" class="ltx_text ltx_font_bold">Jules White<sup id="id28.28.26.1" class="ltx_sup"><span id="id28.28.26.1.1" class="ltx_text ltx_font_medium ltx_font_italic">5</span></sup></span>
 
<span id="id29.29.27" class="ltx_text ltx_font_bold">Shyamal Anadkat<sup id="id29.29.27.1" class="ltx_sup"><span id="id29.29.27.1.1" class="ltx_text ltx_font_medium ltx_font_italic">2</span></sup></span>  
<span id="id30.30.28" class="ltx_text ltx_font_bold">Alexander Hoyle<sup id="id30.30.28.1" class="ltx_sup"><span id="id30.30.28.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  
<span id="id31.31.29" class="ltx_text ltx_font_bold">Philip Resnik<sup id="id31.31.29.1" class="ltx_sup"><span id="id31.31.29.1.1" class="ltx_text ltx_font_medium ltx_font_italic">1</span></sup></span>  

<br class="ltx_break"><sup id="id46.46.id3" class="ltx_sup"><span id="id46.46.id3.1" class="ltx_text ltx_font_italic">1</span></sup> University of Maryland   
<sup id="id47.47.id4" class="ltx_sup"><span id="id47.47.id4.1" class="ltx_text ltx_font_italic">2</span></sup> OpenAI   
<sup id="id48.48.id5" class="ltx_sup"><span id="id48.48.id5.1" class="ltx_text ltx_font_italic">3</span></sup> Stanford   
<sup id="id49.49.id6" class="ltx_sup"><span id="id49.49.id6.1" class="ltx_text ltx_font_italic">4</span></sup> Microsoft   
<sup id="id50.50.id7" class="ltx_sup"><span id="id50.50.id7.1" class="ltx_text ltx_font_italic">5</span></sup> Vanderbilt   
<sup id="id51.51.id8" class="ltx_sup"><span id="id51.51.id8.1" class="ltx_text ltx_font_italic">6</span></sup> Princeton   

<br class="ltx_break"><sup id="id52.52.id9" class="ltx_sup"><span id="id52.52.id9.1" class="ltx_text ltx_font_italic">7</span></sup> Texas State University   
<sup id="id53.53.id10" class="ltx_sup"><span id="id53.53.id10.1" class="ltx_text ltx_font_italic">8</span></sup> Icahn School of Medicine   
<sup id="id54.54.id11" class="ltx_sup"><span id="id54.54.id11.1" class="ltx_text ltx_font_italic">9</span></sup> ASST Brianza    
<br class="ltx_break"><sup id="id55.55.id12" class="ltx_sup"><span id="id55.55.id12.1" class="ltx_text ltx_font_italic">10</span></sup> Mount Sinai Beth Israel   
<sup id="id56.56.id13" class="ltx_sup"><span id="id56.56.id13.1" class="ltx_text ltx_font_italic">11</span></sup> Instituto de Telecomunicações   
<sup id="id57.57.id14" class="ltx_sup"><span id="id57.57.id14.1" class="ltx_text ltx_font_italic">12</span></sup> University of Massachusetts Amherst   
<br class="ltx_break"><span id="id58.58.id15" class="ltx_text ltx_font_typewriter">sschulho@umd.edu</span>    
<span id="id59.59.id16" class="ltx_text ltx_font_typewriter">milie@umd.edu</span>
   
<span id="id60.60.id17" class="ltx_text ltx_font_typewriter">resnik@umd.edu</span>

<br class="ltx_break">
</span></span>
</div>
<div class="ltx_dates">(November 2023)</div>

<div class="ltx_abstract">
<h6 class="ltx_title ltx_title_abstract">Abstract</h6>
<p id="id61.id1" class="ltx_p">Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area’s nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting.</p>
</div>
<nav class="ltx_TOC ltx_list_toc ltx_toc_toc"><h6 class="ltx_title ltx_title_contents">Contents</h6>
<ol class="ltx_toclist">
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch1" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1 </span>Introduction</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section"><a href="#Ch1.S1" title="In 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1.1 </span>What is a Prompt?</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch1.S2" title="In 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1.2 </span>Terminology</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch1.S2.SS1" title="In 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1.2.1 </span>Components of a Prompt</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch1.S2.SS2" title="In 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1.2.2 </span>Prompting Terms</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#Ch1.S3" title="In 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">1.3 </span>A Short History of Prompts</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch2" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2 </span>A Meta-Analysis of Prompting</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch2.S1" title="In 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.1 </span>Systematic Review Process</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S1.SS1" title="In 2.1 Systematic Review Process ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.1.1 </span>The Pipeline</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch2.S2" title="In 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2 </span>Text-Based Techniques</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS1" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.1 </span>In-Context Learning (ICL)</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS2" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.2 </span>Zero-Shot</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS3" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.3 </span>Thought Generation</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS4" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.4 </span>Decomposition</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS5" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.5 </span>Ensembling</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S2.SS6" title="In 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.2.6 </span>Self-Criticism</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch2.S3" title="In 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.3 </span>Prompting Technique Usage</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S3.SS1" title="In 2.3 Prompting Technique Usage ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.3.1 </span>Benchmarks</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#Ch2.S4" title="In 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.4 </span>Prompt Engineering</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch2.S5" title="In 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.5 </span>Answer Engineering</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S5.SS1" title="In 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.5.1 </span>Answer Shape</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S5.SS2" title="In 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.5.2 </span>Answer Space</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch2.S5.SS3" title="In 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">2.5.3 </span>Answer Extractor</span></a></li>
</ol>
</li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch3" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3 </span>Beyond English Text Prompting</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch3.S1" title="In 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1 </span>Multilingual</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S1.SS1" title="In 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1.1 </span>Chain-of-Thought (CoT)</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S1.SS2" title="In 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1.2 </span>In-Context Learning</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S1.SS3" title="In 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1.3 </span>In-Context Example Selection</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S1.SS4" title="In 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1.4 </span>Prompt Template Language Selection</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S1.SS5" title="In 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.1.5 </span>Prompting for Machine Translation</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch3.S2" title="In 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2 </span>Multimodal</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S2.SS1" title="In 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2.1 </span>Image Prompting</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S2.SS2" title="In 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2.2 </span>Audio Prompting</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S2.SS3" title="In 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2.3 </span>Video Prompting</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S2.SS4" title="In 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2.4 </span>Segmentation Prompting</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch3.S2.SS5" title="In 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">3.2.5 </span>3D Prompting</span></a></li>
</ol>
</li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch4" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4 </span>Extensions of Prompting</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch4.S1" title="In 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.1 </span>Agents</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S1.SS1" title="In 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.1.1 </span>Tool Use Agents</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S1.SS2" title="In 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.1.2 </span>Code-Generation Agents</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S1.SS3" title="In 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.1.3 </span>Observation-Based Agents</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S1.SS4" title="In 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.1.4 </span>Retrieval Augmented Generation (RAG)</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch4.S2" title="In 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.2 </span>Evaluation</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S2.SS1" title="In 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.2.1 </span>Prompting Techniques</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S2.SS2" title="In 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.2.2 </span>Output Format</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S2.SS3" title="In 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.2.3 </span>Prompting Frameworks</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch4.S2.SS4" title="In 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">4.2.4 </span>Other Methodologies</span></a></li>
</ol>
</li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch5" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5 </span>Prompting Issues</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch5.S1" title="In 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.1 </span>Security</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S1.SS1" title="In 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.1.1 </span>Types of Prompt Hacking</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S1.SS2" title="In 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.1.2 </span>Risks of Prompt Hacking</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S1.SS3" title="In 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.1.3 </span>Hardening Measures</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch5.S2" title="In 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.2 </span>Alignment</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S2.SS1" title="In 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.2.1 </span>Prompt Sensitivity</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S2.SS2" title="In 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.2.2 </span>Overconfidence and Calibration</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S2.SS3" title="In 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.2.3 </span>Biases, Stereotypes, and Culture</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch5.S2.SS4" title="In 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">5.2.4 </span>Ambiguity</span></a></li>
</ol>
</li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter">
<a href="#Ch6" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6 </span>Benchmarking</span></a>
<ol class="ltx_toclist ltx_toclist_chapter">
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch6.S1" title="In 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1 </span>Technique Benchmarking</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S1.SS1" title="In 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1.1 </span>Comparing Prompting Techniques</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S1.SS2" title="In 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1.2 </span>Question Formats</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S1.SS3" title="In 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1.3 </span>Self-Consistency</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S1.SS4" title="In 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1.4 </span>Evaluating Responses</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S1.SS5" title="In 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.1.5 </span>Results</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#Ch6.S2" title="In 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.2 </span>Prompt Engineering Case Study</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S2.SS1" title="In 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.2.1 </span>Problem</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S2.SS2" title="In 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.2.2 </span>The Dataset</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S2.SS3" title="In 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.2.3 </span>The Process</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#Ch6.S2.SS4" title="In 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">6.2.4 </span>Discussion</span></a></li>
</ol>
</li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_chapter"><a href="#Ch7" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">7 </span>Related Work</span></a></li>
<li class="ltx_tocentry ltx_tocentry_chapter"><a href="#Ch8" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">8 </span>Conclusions</span></a></li>
<li class="ltx_tocentry ltx_tocentry_appendix">
<a href="#A1" title="In The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A </span>Appendices</span></a>
<ol class="ltx_toclist ltx_toclist_appendix">
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S1" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.1 </span>Definitions of Prompting</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#A1.S2" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.2 </span>Extended Vocabulary</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S2.SS1" title="In A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.2.1 </span>Prompting Terms</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S2.SS2" title="In A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.2.2 </span>Prompt Engineering Terms</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S2.SS3" title="In A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.2.3 </span>Fine-Tuning Terms</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S2.SS4" title="In A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.2.4 </span>Orthogonal Prompt Types</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#A1.S3" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3 </span>Datasheet</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS1" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.1 </span>Motivation</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS2" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.2 </span>Composition</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS3" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.3 </span>Collection Process</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS4" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.4 </span>Preprocessing/ Cleaning/ Labeling</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS5" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.5 </span>Uses</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS6" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.6 </span>Distribution</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S3.SS7" title="In A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.3.7 </span>Maintenance</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S4" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.4 </span>Keywords</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S5" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.5 </span>Evaluation Table</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section">
<a href="#A1.S6" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.6 </span>Entrapment Prompting Process</span></a>
<ol class="ltx_toclist ltx_toclist_section">
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S6.SS1" title="In A.6 Entrapment Prompting Process ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.6.1 </span>Exploration</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S6.SS2" title="In A.6 Entrapment Prompting Process ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.6.2 </span>Getting a Label</span></a></li>
<li class="ltx_tocentry ltx_tocentry_subsection"><a href="#A1.S6.SS3" title="In A.6 Entrapment Prompting Process ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.6.3 </span>Varying Prompting Techniques</span></a></li>
</ol>
</li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S7" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.7 </span>Formally Defining a Prompt</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S8" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.8 </span>In-Context Learning Definitions Disambiguation</span></a></li>
<li class="ltx_tocentry ltx_tocentry_section"><a href="#A1.S9" title="In Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_title"><span class="ltx_tag ltx_tag_ref">A.9 </span>Contributions</span></a></li>
</ol>
</li>
</ol></nav>
<section id="Ch1" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">1   Introduction</h2>

<div id="Ch1.p1" class="ltx_para">
<p id="Ch1.p1.1" class="ltx_p">Transformer-based LLMs are widely deployed in consumer-facing, internal, and research settings <cite class="ltx_cite ltx_citemacro_cite">Bommasani et al. (<a href="#bib.bib18" title="" class="ltx_ref">2021</a>)</cite>. Typically, these models rely on the user providing an input “prompt” to which the model produces an output in response. Such prompts may be textual—“Write a poem about trees.”—or take other forms: images, audio, videos, or a combination thereof. The ability to prompt models, particularly prompting with natural language, makes them easy to interact with and use flexibly across a wide range of use cases.</p>
</div>
<div id="Ch1.p2" class="ltx_para">
<p id="Ch1.p2.1" class="ltx_p">Knowing how to effectively structure, evaluate, and perform other tasks with prompts is essential to using these models. Empirically, better prompts lead to improved results across a wide range of tasks <cite class="ltx_cite ltx_citemacro_cite">Wei et al. (<a href="#bib.bib299" title="" class="ltx_ref">2022</a>); Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>); Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>.
A large body of literature has grown around the use of prompting to improve results and the number of prompting techniques is rapidly increasing.</p>
</div>
<div id="Ch1.p3" class="ltx_para">
<p id="Ch1.p3.1" class="ltx_p">However, as prompting is an emerging field, the use of prompts continues to be poorly understood, with only a fraction of existing terminologies and techniques being well-known among practitioners. We perform a large-scale review of prompting techniques to create a robust resource of terminology and techniques in the field. We expect this to be the first iteration of terminologies that will develop over time.</p>
</div>
<section id="Ch1.S0.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Scope of Study</h6>

<div id="Ch1.S0.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px1.p1.1" class="ltx_p">We create a broad directory of prompting techniques, which can be quickly understood and easily implemented for rapid experimentation by developers and researchers. To this end, we limit our study to focus on discrete prefix prompts <cite class="ltx_cite ltx_citemacro_cite">Shin et al. (<a href="#bib.bib259" title="" class="ltx_ref">2020a</a>)</cite> rather than cloze prompts <cite class="ltx_cite ltx_citemacro_cite">Petroni et al. (<a href="#bib.bib212" title="" class="ltx_ref">2019</a>); Cui et al. (<a href="#bib.bib42" title="" class="ltx_ref">2021</a>)</cite>, because modern LLM architectures (especially decoder-only models), which use prefix prompts, are widely used and have robust support for both consumers and researchers.
Additionally, we refined our focus to hard (discrete) prompts rather than soft (continuous) prompts and leave out papers that make use of techniques using gradient-based updates (i.e. fine-tuning).
Finally, we only study task-agnostic techniques.
These decisions keep the work approachable to less technical readers and maintain a manageable scope.</p>
</div>
</section>
<section id="Ch1.S0.SS0.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Sections Overview</h6>

<div id="Ch1.S0.SS0.SSS0.Px2.p1" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px2.p1.1" class="ltx_p">We conducted a machine-assisted systematic review grounded in the PRISMA process <cite class="ltx_cite ltx_citemacro_cite">Page et al. (<a href="#bib.bib199" title="" class="ltx_ref">2021</a>)</cite> (Section <a href="#Ch2.S1" title="2.1 Systematic Review Process ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.1</span></a>) to identify 58 different text-based prompting techniques, from which we create a taxonomy with a robust terminology of prompting terms (Section <a href="#Ch1.S2" title="1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2</span></a>).</p>
</div>
<figure id="Ch1.F1" class="ltx_figure"><img src="/html/2406.06608/assets/x1.png" id="Ch1.F1.g1" class="ltx_graphics ltx_centering ltx_img_square" width="461" height="417" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 1.1: </span>Categories within the field of prompting are interconnected. We discuss 7 core categories that are well described by the papers within our scope.</figcaption>
</figure>
<div id="Ch1.S0.SS0.SSS0.Px2.p2" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px2.p2.1" class="ltx_p">While much literature on prompting focuses on English-only settings, we also discuss multilingual techniques (Section <a href="#Ch3.S1" title="3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1</span></a>). Given the rapid growth in multimodal prompting, where prompts may include media such as images, we also expand our scope to multimodal techniques (Section <a href="#Ch3.S2" title="3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2</span></a>). Many multilingual and multimodal prompting techniques are direct extensions of English text-only prompting techniques.</p>
</div>
<div id="Ch1.S0.SS0.SSS0.Px2.p3" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px2.p3.1" class="ltx_p">As prompting techniques grow more complex, they have begun to incorporate external tools, such as Internet browsing and calculators.
We use the term ‘agents‘ to describe these types of prompting techniques (Section <a href="#Ch4.S1" title="4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1</span></a>).</p>
</div>
<div id="Ch1.S0.SS0.SSS0.Px2.p4" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px2.p4.1" class="ltx_p">It is important to understand how to evaluate the outputs of agents and prompting techniques to ensure accuracy and avoid hallucinations. Thus, we discuss ways of evaluating these outputs (Section <a href="#Ch4.S2" title="4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2</span></a>). We also discuss security (Section <a href="#Ch5.S1" title="5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1</span></a>) and safety measures (Section <a href="#Ch5.S2" title="5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2</span></a>) for designing prompts that reduce the risk of harm to companies and users.</p>
</div>
<div id="Ch1.S0.SS0.SSS0.Px2.p5" class="ltx_para">
<p id="Ch1.S0.SS0.SSS0.Px2.p5.1" class="ltx_p">Finally, we apply prompting techniques in two case studies (Section <a href="#Ch6.S1" title="6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.1</span></a>). In the first, we test a range of prompting techniques against the commonly used benchmark MMLU <cite class="ltx_cite ltx_citemacro_cite">Hendrycks et al. (<a href="#bib.bib89" title="" class="ltx_ref">2021</a>)</cite>. In the second, we explore in detail an example of manual prompt engineering on a significant, real-world use case, identifying signals of frantic hopelessness–a top indicator of suicidal crisis–in the text of individuals seeking support <cite class="ltx_cite ltx_citemacro_cite">Schuck et al. (<a href="#bib.bib246" title="" class="ltx_ref">2019a</a>)</cite>.
We conclude with a discussion of the nature of prompting and its recent development (Section <a href="#Ch8" title="8 Conclusions ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">8</span></a>).</p>
</div>
</section>
<section id="Ch1.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">1.1 </span>What is a Prompt?</h3>

<div id="Ch1.S1.p1" class="ltx_para">
<p id="Ch1.S1.p1.1" class="ltx_p">A prompt is an input to a Generative AI model, that is used to guide its output <cite class="ltx_cite ltx_citemacro_cite">Meskó (<a href="#bib.bib181" title="" class="ltx_ref">2023</a>); White et al. (<a href="#bib.bib305" title="" class="ltx_ref">2023</a>); Heston and Khun (<a href="#bib.bib92" title="" class="ltx_ref">2023</a>); Hadi et al. (<a href="#bib.bib83" title="" class="ltx_ref">2023</a>); Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>. Prompts may consist of text, image, sound, or other media. Some examples of prompts include: “write a three paragraph email for a marketing campaign for an accounting firm”, a photograph of a table accompanied by the text “describe everything on the table”, or a recording of an online meeting, with the instructions “summarize this”.</p>
</div>
<section id="Ch1.S1.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Template</h6>

<div id="Ch1.S1.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="Ch1.S1.SS0.SSS0.Px1.p1.1" class="ltx_p">Prompts are often constructed via a prompt template <cite class="ltx_cite ltx_citemacro_cite">Shin et al. (<a href="#bib.bib260" title="" class="ltx_ref">2020b</a>)</cite>. A prompt template is a function that contains one or more variables which will be replaced by some media (usually text) to create a prompt. This prompt can then be considered to be an <span id="Ch1.S1.SS0.SSS0.Px1.p1.1.1" class="ltx_text ltx_font_italic">instance</span> of the template.</p>
</div>
<div id="Ch1.S1.SS0.SSS0.Px1.p2" class="ltx_para">
<p id="Ch1.S1.SS0.SSS0.Px1.p2.1" class="ltx_p">Consider applying prompting to the task of binary classification of tweets. Here is an initial prompt template that can be used to classify inputs.</p>
</div>
<div id="Ch1.S1.SS0.SSS0.Px1.p3" class="ltx_para ltx_noindent">
<svg id="Ch1.S1.SS0.SSS0.Px1.p3.pic1" class="ltx_picture" height="58" overflow="visible" version="1.1" width="600"><g transform="translate(0,58) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 58 L 600 58 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 56.03 L 598.03 56.03 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="30.44" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S1.SS0.SSS0.Px1.p3.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S1.SS0.SSS0.Px1.p3.pic1.1.1.1.1.1.1" class="ltx_p">Classify the tweet as positive or negative:</span>
<span id="Ch1.S1.SS0.SSS0.Px1.p3.pic1.1.1.1.1.1.2" class="ltx_p">{TWEET}</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch1.S1.SS0.SSS0.Px1.p4" class="ltx_para">
<p id="Ch1.S1.SS0.SSS0.Px1.p4.1" class="ltx_p">Each tweet in the dataset would be inserted into a separate instance of the template and the resulting prompt would be given to a LLM for inference.</p>
</div>
<figure id="Ch1.F2" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><svg id="Ch1.F2.pic1" class="ltx_picture ltx_centering ltx_figure_panel" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.F2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.F2.pic1.1.1.1.1.1.1" class="ltx_p">Write a poem about trees.</span>
</span></foreignObject></g></g></svg></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1"><svg id="Ch1.F2.pic2" class="ltx_picture ltx_centering ltx_figure_panel" height="41.4" overflow="visible" version="1.1" width="600"><g transform="translate(0,41.4) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 41.4 L 600 41.4 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 39.43 L 598.03 39.43 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="13.84" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.F2.pic2.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.F2.pic2.1.1.1.1.1.1" class="ltx_p">Write a poem about the following topic: {USER_INPUT}</span>
</span></foreignObject></g></g></svg></div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 1.2: </span>Prompts and prompt templates are distinct concepts; a prompt template becomes a prompt when input is inserted into it.</figcaption>
</figure>
</section>
</section>
<section id="Ch1.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">1.2 </span>Terminology</h3>

<figure id="Ch1.F3" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch1.F3.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch1.F3.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
font=, rectangle,
draw=black, rounded corners,
align=left,
minimum width=2em, edge+=darkgray, line width=1pt,
s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
text width=7em, ,
[Prompt <a href="#Ch1.S1" title="1.1 What is a Prompt? ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.1</span></a>, text width=3em , fill=teal!50
[Prompting <a href="#Ch1.S2.SS2.SSS0.Px1" title="Prompting ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=red!50
[Context <a href="#Ch1.S2.SS1.SSS0.Px6" title="Additional Information ‣ 1.2.1 Components of a Prompt ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.1</span></a>, fill=red!40]
[Context Window <a href="#A1.S2.SS1.SSS0.Px1" title="Context Window ‣ A.2.1 Prompting Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.1</span></a>, fill=red!40]
[Priming <a href="#A1.S2.SS1.SSS0.Px2" title="Priming ‣ A.2.1 Prompting Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.1</span></a>, fill=red!40]
[Prompting Technique 
<br class="ltx_break"><a href="#Ch1.S2.SS2.SSS0.Px3" title="Prompting Technique ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=red!40
[In-Context Learning 
<br class="ltx_break"><a href="#Ch2.S2.SS1" title="2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1</span></a>, fill=red!30
[Few-Shot Prompt <a href="#Ch2.S2.SS1.SSS0.Px1" title="Few-Shot Prompting ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1</span></a>, fill=red!20]
[Exemplar <a href="#Ch1.S2.SS2.SSS0.Px6" title="Exemplar ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=red!20]
]
[Zero-Shot Prompt <a href="#Ch2.S2.SS2" title="2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!30]
]
[Orthogonal Prompt Types 
<br class="ltx_break"><a href="#A1.S2.SS4" title="A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4</span></a>, fill=red!40
[
Density <a href="#A1.S2.SS4.SSS2" title="A.2.4.2 Hard vs Soft Prompts ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.2</span></a>, fill=red!30
[Continuous Prompt 
<br class="ltx_break"><a href="#A1.S2.SS4.SSS2.Px2" title="Soft (continuous) Prompt ‣ A.2.4.2 Hard vs Soft Prompts ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.2</span></a>, fill=red!20]
[Discrete Prompt <a href="#A1.S2.SS4.SSS2.Px1" title="Hard (discrete) Prompt ‣ A.2.4.2 Hard vs Soft Prompts ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.2</span></a>, fill=red!20]
]
[
Originator <a href="#A1.S2.SS4.SSS1" title="A.2.4.1 Originator ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.1</span></a>, fill=red!30
[User Prompt <a href="#A1.S2.SS4.SSS1.Px1" title="User Prompt ‣ A.2.4.1 Originator ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.1</span></a>, fill=red!20]
[System Prompt <a href="#A1.S2.SS4.SSS1.Px3" title="System Prompt ‣ A.2.4.1 Originator ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.1</span></a>, fill=red!20]
[Assistant Prompt <a href="#A1.S2.SS4.SSS1.Px2" title="Assistant Prompt ‣ A.2.4.1 Originator ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.1</span></a>, fill=red!20]
]
[
Prediction Style <a href="#A1.S2.SS4.SSS3" title="A.2.4.3 Prediction Styles ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.3</span></a>, fill=red!30
[Prefix <a href="#A1.S2.SS4.SSS3.Px2" title="Prefix ‣ A.2.4.3 Prediction Styles ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.3</span></a>, fill=red!20]
[Cloze <a href="#A1.S2.SS4.SSS3.Px1" title="Cloze ‣ A.2.4.3 Prediction Styles ‣ A.2.4 Orthogonal Prompt Types ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.4.3</span></a>, fill=red!20]
]
]
[
Prompt Chain <a href="#Ch1.S2.SS2.SSS0.Px2" title="Prompt Chain ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=red!40
]
]
[Prompt Template <a href="#Ch1.S1.SS0.SSS0.Px1" title="Prompt Template ‣ 1.1 What is a Prompt? ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.1</span></a>, fill=blue!50]
[
Prompt Engineering <a href="#Ch1.S2.SS2.SSS0.Px4" title="Prompt Engineering ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=green!50
[
Prompt Engineering 
<br class="ltx_break">Technique <a href="#Ch1.S2.SS2.SSS0.Px5" title="Prompt Engineering Technique ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.2.2</span></a>, fill=green!40
]
[
Meta-Prompting <a href="#Ch2.S4.SS0.SSS0.Px1" title="Meta Prompting ‣ 2.4 Prompt Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.4</span></a>, fill=green!40
]
[
Answer Engineering 
<br class="ltx_break"><a href="#Ch2.S5" title="2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5</span></a>, fill=green!40
[Verbalizer <a href="#Ch2.S5.SS3.SSS0.Px1" title="Verbalizer ‣ 2.5.3 Answer Extractor ‣ 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5.3</span></a>, fill=green!30]
[Extractor <a href="#Ch2.S5.SS3" title="2.5.3 Answer Extractor ‣ 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5.3</span></a>, fill=green!30]
]
[Conversational Prompt 
<br class="ltx_break">Engineering <a href="#A1.S2.SS2.SSS0.Px1" title="Conversational Prompt Engineering ‣ A.2.2 Prompt Engineering Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.2</span></a>, fill=green!30]
]
[
Fine-Tuning <a href="#A1.S2.SS3" title="A.2.3 Fine-Tuning Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.3</span></a>, fill=orange!50
[Prompt-Based 
<br class="ltx_break">Learning <a href="#A1.S2.SS3.SSS0.Px1" title="Prompt-Based Learning ‣ A.2.3 Fine-Tuning Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.3</span></a>, fill=orange!40]
[Prompt Tuning <a href="#A1.S2.SS3.SSS0.Px2" title="Prompt Tuning ‣ A.2.3 Fine-Tuning Terms ‣ A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2.3</span></a>, fill=orange!40]
]
]

.</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 1.3: </span>A Terminology of prompting. Terms with links to the appendix are not sufficiently critical to describe in the main paper, but are important to the field of prompting. Prompting techniques are shown in Figure <a href="#Ch2.F2" title="Figure 2.2 ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2</span></a></figcaption>
</figure>
<section id="Ch1.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">1.2.1 </span>Components of a Prompt</h4>

<div id="Ch1.S2.SS1.p1" class="ltx_para">
<p id="Ch1.S2.SS1.p1.1" class="ltx_p">There are a variety of common components included in a prompt. We summarize the most commonly used components and discuss how they fit into prompts.</p>
</div>
<section id="Ch1.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Directive</h6>

<div id="Ch1.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px1.p1.1" class="ltx_p">Many prompts issue a directive in the form of an instruction or question.<span id="Ch1.footnote1" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">1</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">1</sup><span class="ltx_tag ltx_tag_note">1</span>“Directives”, from <cite class="ltx_cite ltx_citemacro_citet">Searle (<a href="#bib.bib254" title="" class="ltx_ref">1969</a>)</cite>, are a type of speech act intended to encourage an action, and have been invoked in models of human-computer dialogue <cite class="ltx_cite ltx_citemacro_citet">Morelli et al. (<a href="#bib.bib186" title="" class="ltx_ref">1991</a>)</cite>.</span></span></span> This is the core intent of the prompt, sometimes simply called the "intent". For example, here is an example of a prompt with a single instruction:</p>
</div>
<div id="Ch1.S2.SS1.SSS0.Px1.p2" class="ltx_para ltx_noindent">
<svg id="Ch1.S2.SS1.SSS0.Px1.p2.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S2.SS1.SSS0.Px1.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S2.SS1.SSS0.Px1.p2.pic1.1.1.1.1.1.1" class="ltx_p">Tell me five good books to read.</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch1.S2.SS1.SSS0.Px1.p3" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px1.p3.1" class="ltx_p">Directives can also be implicit, as in this one-shot case, where the directive is to perform English to Spanish translation:</p>
</div>
<div id="Ch1.S2.SS1.SSS0.Px1.p4" class="ltx_para ltx_noindent">
<svg id="Ch1.S2.SS1.SSS0.Px1.p4.pic1" class="ltx_picture" height="56.31" overflow="visible" version="1.1" width="600"><g transform="translate(0,56.31) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 56.31 L 600 56.31 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 54.34 L 598.03 54.34 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="28.75" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S2.SS1.SSS0.Px1.p4.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S2.SS1.SSS0.Px1.p4.pic1.1.1.1.1.1.1" class="ltx_p">Night: Noche</span>
<span id="Ch1.S2.SS1.SSS0.Px1.p4.pic1.1.1.1.1.1.2" class="ltx_p">Morning:</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch1.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Examples</h6>

<div id="Ch1.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px2.p1.1" class="ltx_p">Examples, also known as exemplars or shots, act as demonstrations that guide the GenAI to accomplish a task. The above prompt is a One-Shot (i.e. one example) prompt.</p>
</div>
</section>
<section id="Ch1.S2.SS1.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Output Formatting</h6>

<div id="Ch1.S2.SS1.SSS0.Px3.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px3.p1.1" class="ltx_p">It is often desirable for the GenAI to output information in certain formats, for example, CSVs or markdown formats <cite class="ltx_cite ltx_citemacro_cite">Xia et al. (<a href="#bib.bib314" title="" class="ltx_ref">2024</a>)</cite>. To facilitate this, you can simply add instructions to do so as seen below:</p>
</div>
<div id="Ch1.S2.SS1.SSS0.Px3.p2" class="ltx_para ltx_noindent">
<svg id="Ch1.S2.SS1.SSS0.Px3.p2.pic1" class="ltx_picture" height="53.77" overflow="visible" version="1.1" width="600"><g transform="translate(0,53.77) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 53.77 L 600 53.77 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 51.8 L 598.03 51.8 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="26.21" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S2.SS1.SSS0.Px3.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S2.SS1.SSS0.Px3.p2.pic1.1.1.1.1.1.1" class="ltx_p">{PARAGRAPH}</span>
<span id="Ch1.S2.SS1.SSS0.Px3.p2.pic1.1.1.1.1.1.2" class="ltx_p">Summarize this into a CSV.</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch1.S2.SS1.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Style Instructions</h6>

<div id="Ch1.S2.SS1.SSS0.Px4.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px4.p1.1" class="ltx_p">Style instructions are a type of output formatting used to modify the output stylistically rather than structurally (Section <a href="#Ch2.S2.SS2.SSS0.Px2" title="Style Prompting ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>). For example:</p>
</div>
<div id="Ch1.S2.SS1.SSS0.Px4.p2" class="ltx_para ltx_noindent">
<svg id="Ch1.S2.SS1.SSS0.Px4.p2.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S2.SS1.SSS0.Px4.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S2.SS1.SSS0.Px4.p2.pic1.1.1.1.1.1.1" class="ltx_p">Write a clear and curt paragraph about llamas.</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch1.S2.SS1.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Role</h6>

<div id="Ch1.S2.SS1.SSS0.Px5.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px5.p1.1" class="ltx_p">A Role, also known as a persona <cite class="ltx_cite ltx_citemacro_cite">Schmidt et al. (<a href="#bib.bib245" title="" class="ltx_ref">2023</a>); Wang et al. (<a href="#bib.bib298" title="" class="ltx_ref">2023l</a>)</cite>, is a frequently discussed component that can improve writing and style text (Section <a href="#Ch2.S2.SS2.SSS0.Px1" title="Role Prompting ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>). For example:</p>
</div>
<div id="Ch1.S2.SS1.SSS0.Px5.p2" class="ltx_para ltx_noindent">
<svg id="Ch1.S2.SS1.SSS0.Px5.p2.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S2.SS1.SSS0.Px5.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S2.SS1.SSS0.Px5.p2.pic1.1.1.1.1.1.1" class="ltx_p">Pretend you are a shepherd and write a limerick about llamas.</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch1.S2.SS1.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Additional Information</h6>

<div id="Ch1.S2.SS1.SSS0.Px6.p1" class="ltx_para">
<p id="Ch1.S2.SS1.SSS0.Px6.p1.1" class="ltx_p">It is often necessary to include additional information in the prompt. For example, if the directive is to write an email, you might include information such as your name and position so the GenAI can properly sign the email. Additional Information is sometimes called ‘context‘, though we discourage the use of this term as it is overloaded with other meanings in the prompting space<span id="Ch1.footnote2" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">2</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">2</sup><span class="ltx_tag ltx_tag_note">2</span>e.g. the context is the tokens processed by the LLM in a forward pass</span></span></span>.</p>
</div>
</section>
</section>
<section id="Ch1.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">1.2.2 </span>Prompting Terms</h4>

<div id="Ch1.S2.SS2.p1" class="ltx_para">
<p id="Ch1.S2.SS2.p1.1" class="ltx_p">Terminology within the prompting literature is rapidly developing. As it stands, there are many poorly understood definitions (e.g. prompt, prompt engineering) and conflicting ones (e.g. role prompt vs persona prompt). The lack of a consistent vocabulary hampers the community’s ability to clearly describe the various prompting techniques in use. We provide a robust vocabulary of terms used in the prompting community (Figure <a href="#Ch1.F3" title="Figure 1.3 ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.3</span></a>).<span id="Ch1.footnote3" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">3</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">3</sup><span class="ltx_tag ltx_tag_note">3</span>By robust, we mean that it covers most existing commonly used terms in the field.</span></span></span> Less frequent terms are left to Appendix <a href="#A1.S2" title="A.2 Extended Vocabulary ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.2</span></a>. In order to accurately define frequently-used terms like prompt and prompt engineering, we integrate many definitions (Appendix <a href="#A1.S1" title="A.1 Definitions of Prompting ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.1</span></a>) to derive representative definitions.</p>
</div>
<section id="Ch1.S2.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompting</h6>

<div id="Ch1.S2.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px1.p1.1" class="ltx_p">Prompting is the process of providing a prompt to a GenAI, which then generates a response. For example, the action of sending a chunk of text or uploading an image constitutes prompting.</p>
</div>
</section>
<section id="Ch1.S2.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Chain</h6>

<div id="Ch1.S2.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px2.p1.1" class="ltx_p">A prompt chain (activity: prompt chaining) consists of two or more prompt templates used in succession. The output of the prompt generated by the first prompt template is used to parameterize the second template, continuing until all templates are exhausted <cite class="ltx_cite ltx_citemacro_cite">Wu et al. (<a href="#bib.bib312" title="" class="ltx_ref">2022</a>)</cite>.</p>
</div>
</section>
<section id="Ch1.S2.SS2.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompting Technique</h6>

<div id="Ch1.S2.SS2.SSS0.Px3.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px3.p1.1" class="ltx_p">A prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multiple prompts. A prompting technique may incorporate conditional or branching logic, parallelism, or other architectural considerations spanning multiple prompts.</p>
</div>
</section>
<section id="Ch1.S2.SS2.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Engineering</h6>

<div id="Ch1.S2.SS2.SSS0.Px4.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px4.p1.1" class="ltx_p">Prompt engineering is the iterative process of developing a prompt by modifying or changing the prompting technique that you are using (Figure <a href="#Ch1.F4" title="Figure 1.4 ‣ Prompt Engineering ‣ 1.2.2 Prompting Terms ‣ 1.2 Terminology ‣ 1 Introduction ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">1.4</span></a>).</p>
</div>
<figure id="Ch1.F4" class="ltx_figure"><img src="/html/2406.06608/assets/x2.png" id="Ch1.F4.g1" class="ltx_graphics ltx_centering ltx_img_square" width="461" height="472" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 1.4: </span>The Prompt Engineering Process consists of three repeated steps 1) performing inference on a dataset 2) evaluating performance and 3) modifying the prompt template. Note that the extractor is used to extract a final response from the LLM output (e.g. "This phrase is positive" <math id="Ch1.F4.2.m1.1" class="ltx_Math" alttext="\rightarrow" display="inline"><semantics id="Ch1.F4.2.m1.1b"><mo stretchy="false" id="Ch1.F4.2.m1.1.1" xref="Ch1.F4.2.m1.1.1.cmml">→</mo><annotation-xml encoding="MathML-Content" id="Ch1.F4.2.m1.1c"><ci id="Ch1.F4.2.m1.1.1.cmml" xref="Ch1.F4.2.m1.1.1">→</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch1.F4.2.m1.1d">\rightarrow</annotation></semantics></math> "positive"). See more information on extractors in Section <a href="#Ch2.S5" title="2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5</span></a>.</figcaption>
</figure>
</section>
<section id="Ch1.S2.SS2.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Engineering Technique</h6>

<div id="Ch1.S2.SS2.SSS0.Px5.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px5.p1.1" class="ltx_p">A prompt engineering technique is a strategy for iterating on a prompt to improve it. In literature, this will often be automated techniques <cite class="ltx_cite ltx_citemacro_cite">Deng et al. (<a href="#bib.bib45" title="" class="ltx_ref">2022</a>)</cite>, but in consumer settings, users often perform prompt engineering manually.</p>
</div>
</section>
<section id="Ch1.S2.SS2.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar</h6>

<div id="Ch1.S2.SS2.SSS0.Px6.p1" class="ltx_para">
<p id="Ch1.S2.SS2.SSS0.Px6.p1.1" class="ltx_p">Exemplars are examples of a task being completed that are shown to a model in a prompt <cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>.</p>
</div>
</section>
</section>
</section>
<section id="Ch1.S3" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">1.3 </span>A Short History of Prompts</h3>

<div id="Ch1.S3.p1" class="ltx_para">
<p id="Ch1.S3.p1.1" class="ltx_p">The idea of using natural language prefixes, or prompts, to elicit language model behaviors and responses originated before the GPT-3 and ChatGPT era. GPT-2 <cite class="ltx_cite ltx_citemacro_cite">Radford et al. (<a href="#bib.bib228" title="" class="ltx_ref">2019a</a>)</cite> makes use of prompts and they appear to be first used in the context of Generative AI by <cite class="ltx_cite ltx_citemacro_citet">Fan et al. (<a href="#bib.bib54" title="" class="ltx_ref">2018</a>)</cite>. However, the concept of prompts was preceded by related concepts such as control codes <cite class="ltx_cite ltx_citemacro_cite">Pfaff (<a href="#bib.bib214" title="" class="ltx_ref">1979</a>); Poplack (<a href="#bib.bib217" title="" class="ltx_ref">1980</a>); Keskar et al. (<a href="#bib.bib112" title="" class="ltx_ref">2019</a>)</cite> and writing prompts.</p>
</div>
<div id="Ch1.S3.p2" class="ltx_para">
<p id="Ch1.S3.p2.1" class="ltx_p">The term Prompt Engineering appears to have come into existence more recently from <cite class="ltx_cite ltx_citemacro_citet">Radford et al. (<a href="#bib.bib227" title="" class="ltx_ref">2021</a>)</cite> then slightly later from <cite class="ltx_cite ltx_citemacro_citet">Reynolds and McDonell (<a href="#bib.bib233" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
<div id="Ch1.S3.p3" class="ltx_para">
<p id="Ch1.S3.p3.1" class="ltx_p">However, various papers perform prompt engineering without naming the term
<cite class="ltx_cite ltx_citemacro_cite">Wallace et al. (<a href="#bib.bib282" title="" class="ltx_ref">2019</a>); Shin et al. (<a href="#bib.bib259" title="" class="ltx_ref">2020a</a>)</cite>, including
<cite class="ltx_cite ltx_citemacro_citet">Schick and Schütze (<a href="#bib.bib242" title="" class="ltx_ref">2020a</a>, <a href="#bib.bib243" title="" class="ltx_ref">b</a>); Gao et al. (<a href="#bib.bib67" title="" class="ltx_ref">2021</a>)</cite> for non-autoregressive language models.</p>
</div>
<div id="Ch1.S3.p4" class="ltx_para">
<p id="Ch1.S3.p4.1" class="ltx_p">Some of the first works on prompting define a prompt slightly differently to how it is currently used. For example, consider the following prompt from <cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>:</p>
</div>
<div id="Ch1.S3.p5" class="ltx_para ltx_noindent">
<svg id="Ch1.S3.p5.pic1" class="ltx_picture" height="53.77" overflow="visible" version="1.1" width="600"><g transform="translate(0,53.77) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 53.77 L 600 53.77 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 51.8 L 598.03 51.8 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="26.21" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch1.S3.p5.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch1.S3.p5.pic1.1.1.1.1.1.1" class="ltx_p">Translate English to French:</span>
<span id="Ch1.S3.p5.pic1.1.1.1.1.1.2" class="ltx_p">llama</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch1.S3.p6" class="ltx_para">
<p id="Ch1.S3.p6.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite> consider the word "llama" to be the prompt, while "Translate English to French:" is the "task description". More recent papers, including this one, refer to the entire string passed to the LLM as the prompt.</p>
</div>
</section>
</section>
<section id="Ch2" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">2   A Meta-Analysis of Prompting</h2>

<section id="Ch2.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">2.1 </span>Systematic Review Process</h3>

<div id="Ch2.S1.p1" class="ltx_para">
<p id="Ch2.S1.p1.1" class="ltx_p">In order to robustly collect a dataset of sources for this paper, we ran a systematic literature review grounded in the PRISMA process <cite class="ltx_cite ltx_citemacro_cite">Page et al. (<a href="#bib.bib199" title="" class="ltx_ref">2021</a>)</cite> (Figure <a href="#Ch2.F1" title="Figure 2.1 ‣ 2.1.1 The Pipeline ‣ 2.1 Systematic Review Process ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.1</span></a>). We host this dataset on <a target="_blank" href="https://huggingface.co/datasets/PromptSystematicReview/Prompt_Systematic_Review_Dataset" title="" class="ltx_ref ltx_href">HuggingFace</a> and present a datasheet <cite class="ltx_cite ltx_citemacro_cite">Gebru et al. (<a href="#bib.bib71" title="" class="ltx_ref">2021</a>)</cite> for the dataset in Appendix <a href="#A1.S3" title="A.3 Datasheet ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.3</span></a>. Our main data sources were <a target="_blank" href="https://arxiv.org/" title="" class="ltx_ref ltx_href">arXiv</a>, <a target="_blank" href="https://semanticscholar.com/" title="" class="ltx_ref ltx_href">Semantic Scholar</a>, and <a target="_blank" href="https://aclanthology.org" title="" class="ltx_ref ltx_href">ACL</a>. We query these databases with a list of 44 keywords narrowly related to prompting and prompt engineering (Appendix <a href="#A1.S4" title="A.4 Keywords ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.4</span></a>).</p>
</div>
<section id="Ch2.S1.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.1.1 </span>The Pipeline</h4>

<div id="Ch2.S1.SS1.p1" class="ltx_para">
<p id="Ch2.S1.SS1.p1.4" class="ltx_p">In this section, we introduce our data scraping pipeline, which includes both human and LLM-assisted review.<span id="Ch2.footnote4" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">4</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">4</sup><span class="ltx_tag ltx_tag_note">4</span>Using <span id="Ch2.footnote4.1" class="ltx_text ltx_font_typewriter">GPT-4-1106-preview</span></span></span></span>
As an initial sample to establish filtering critera, we retrieve papers from arXiv based on a simple set of keywords and boolean rules (<a href="#A1.S4" title="A.4 Keywords ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.4</span></a>).
Then, human annotators label a sample of 1,661 articles from the arXiv set for the following criteria:</p>
<ol id="Ch2.S1.I1" class="ltx_enumerate">
<li id="Ch2.S1.I1.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">1.</span> 
<div id="Ch2.S1.I1.i1.p1" class="ltx_para">
<p id="Ch2.S1.I1.i1.p1.1" class="ltx_p">Does the paper propose a novel prompting technique? (include)</p>
</div>
</li>
<li id="Ch2.S1.I1.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">2.</span> 
<div id="Ch2.S1.I1.i2.p1" class="ltx_para">
<p id="Ch2.S1.I1.i2.p1.1" class="ltx_p">Does the paper strictly cover hard prefix prompts? (include)</p>
</div>
</li>
<li id="Ch2.S1.I1.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">3.</span> 
<div id="Ch2.S1.I1.i3.p1" class="ltx_para">
<p id="Ch2.S1.I1.i3.p1.1" class="ltx_p">Does the paper focus on training by backpropagating gradients? (exclude)</p>
</div>
</li>
<li id="Ch2.S1.I1.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">4.</span> 
<div id="Ch2.S1.I1.i4.p1" class="ltx_para">
<p id="Ch2.S1.I1.i4.p1.1" class="ltx_p">For non-text modalities, does it use a masked frame and/or window? (include)</p>
</div>
</li>
</ol>
<p id="Ch2.S1.SS1.p1.3" class="ltx_p">A set of 300 articles are reviewed independently by two annotators, with 92% agreement (Krippendorff’s <math id="Ch2.S1.SS1.p1.1.m1.1" class="ltx_Math" alttext="\alpha" display="inline"><semantics id="Ch2.S1.SS1.p1.1.m1.1a"><mi id="Ch2.S1.SS1.p1.1.m1.1.1" xref="Ch2.S1.SS1.p1.1.m1.1.1.cmml">α</mi><annotation-xml encoding="MathML-Content" id="Ch2.S1.SS1.p1.1.m1.1b"><ci id="Ch2.S1.SS1.p1.1.m1.1.1.cmml" xref="Ch2.S1.SS1.p1.1.m1.1.1">𝛼</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S1.SS1.p1.1.m1.1c">\alpha</annotation></semantics></math> = Cohen’s <math id="Ch2.S1.SS1.p1.2.m2.1" class="ltx_Math" alttext="\kappa" display="inline"><semantics id="Ch2.S1.SS1.p1.2.m2.1a"><mi id="Ch2.S1.SS1.p1.2.m2.1.1" xref="Ch2.S1.SS1.p1.2.m2.1.1.cmml">κ</mi><annotation-xml encoding="MathML-Content" id="Ch2.S1.SS1.p1.2.m2.1b"><ci id="Ch2.S1.SS1.p1.2.m2.1.1.cmml" xref="Ch2.S1.SS1.p1.2.m2.1.1">𝜅</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S1.SS1.p1.2.m2.1c">\kappa</annotation></semantics></math> = 81%).
Next, we develop a prompt using <span id="Ch2.S1.SS1.p1.3.1" class="ltx_text ltx_font_typewriter">GPT-4-1106-preview</span> to classify the remaining articles. We validate the prompt against 100 ground-truth annotations, achieving 89% precision and 75% recall (for an <math id="Ch2.S1.SS1.p1.3.m3.1" class="ltx_Math" alttext="F1" display="inline"><semantics id="Ch2.S1.SS1.p1.3.m3.1a"><mrow id="Ch2.S1.SS1.p1.3.m3.1.1" xref="Ch2.S1.SS1.p1.3.m3.1.1.cmml"><mi id="Ch2.S1.SS1.p1.3.m3.1.1.2" xref="Ch2.S1.SS1.p1.3.m3.1.1.2.cmml">F</mi><mo lspace="0em" rspace="0em" id="Ch2.S1.SS1.p1.3.m3.1.1.1" xref="Ch2.S1.SS1.p1.3.m3.1.1.1.cmml">​</mo><mn id="Ch2.S1.SS1.p1.3.m3.1.1.3" xref="Ch2.S1.SS1.p1.3.m3.1.1.3.cmml">1</mn></mrow><annotation-xml encoding="MathML-Content" id="Ch2.S1.SS1.p1.3.m3.1b"><apply id="Ch2.S1.SS1.p1.3.m3.1.1.cmml" xref="Ch2.S1.SS1.p1.3.m3.1.1"><times id="Ch2.S1.SS1.p1.3.m3.1.1.1.cmml" xref="Ch2.S1.SS1.p1.3.m3.1.1.1"></times><ci id="Ch2.S1.SS1.p1.3.m3.1.1.2.cmml" xref="Ch2.S1.SS1.p1.3.m3.1.1.2">𝐹</ci><cn type="integer" id="Ch2.S1.SS1.p1.3.m3.1.1.3.cmml" xref="Ch2.S1.SS1.p1.3.m3.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S1.SS1.p1.3.m3.1c">F1</annotation></semantics></math> of 81%).
The combined human and LLM annotations generate a final set of 1,565 papers.</p>
</div>
<figure id="Ch2.F1" class="ltx_figure"><img src="/html/2406.06608/assets/x3.png" id="Ch2.F1.g1" class="ltx_graphics ltx_centering ltx_img_square" width="167" height="175" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.1: </span>The PRISMA review process. We accumulate 4,247 unique records from which we extract 1,565 relevant records.</figcaption>
</figure>
</section>
</section>
<section id="Ch2.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">2.2 </span>Text-Based Techniques</h3>

<div id="Ch2.S2.p1" class="ltx_para">
<p id="Ch2.S2.p1.1" class="ltx_p">We now present a comprehensive taxonomical ontology of 58 text-based prompting techniques, broken into 6 major categories (Figure <a href="#Ch2.F2" title="Figure 2.2 ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2</span></a>). Although some of the techniques might fit into multiple categories, we place them in a single category of most relevance.</p>
</div>
<figure id="Ch2.F2" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch2.F2.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch2.F2.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
font=, rectangle,
draw=black, rounded corners,
align=left,
minimum width=2em, edge+=darkgray, line width=1pt,
s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
text width=7em, ,
[Text-Base Prompt. Tech. , fill=teal!50
[Zero-Shot <a href="#Ch2.S2.SS2" title="2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50
[Emotion Prompting <a href="#Ch2.S2.SS2.SSS0.Px3" title="Emotion Prompting ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[Role Prompting <a href="#Ch2.S2.SS2.SSS0.Px1" title="Role Prompting ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[Style Prompting <a href="#Ch2.S2.SS2.SSS0.Px2" title="Style Prompting ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[S2A <a href="#Ch2.S2.SS2.SSS0.Px4" title="System 2 Attention (S2A) ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[SimToM <a href="#Ch2.S2.SS2.SSS0.Px5" title="SimToM ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[RaR <a href="#Ch2.S2.SS2.SSS0.Px6" title="Rephrase and Respond (RaR) ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[RE2 <a href="#Ch2.S2.SS2.SSS0.Px7" title="Re-reading (RE2) ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
[Self-Ask <a href="#Ch2.S2.SS2.SSS0.Px8" title="Self-Ask ‣ 2.2.2 Zero-Shot ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.2</span></a>, fill=red!50]
]
[Few-Shot <a href="#Ch2.S2.SS1.SSS0.Px1" title="Few-Shot Prompting ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1</span></a>, fill=blue!50
[Example Generation, fill=blue!40
[SG-ICL <a href="#Ch2.S2.SS1.SSS2.Px3" title="Self-Generated In-Context Learning (SG-ICL) ‣ 2.2.1.2 Few-Shot Prompting Techniques ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.2</span></a>, fill=blue!30]
]
[Example Ordering <a href="#Ch2.S2.SS1.SSS1.Px2" title="Exemplar Ordering ‣ 2.2.1.1 Few-Shot Prompting Design Decisions ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.1</span></a>, fill=blue!40]
[Exemplar Selection
<br class="ltx_break"><a href="#Ch2.S2.SS1.SSS2" title="2.2.1.2 Few-Shot Prompting Techniques ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.2</span></a>, fill=blue!40
[KNN <a href="#Ch2.S2.SS1.SSS2.Px1" title="K-Nearest Neighbor (KNN) ‣ 2.2.1.2 Few-Shot Prompting Techniques ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.2</span></a>, fill=blue!30]
[Vote-K <a href="#Ch2.S2.SS1.SSS2.Px2" title="Vote-K ‣ 2.2.1.2 Few-Shot Prompting Techniques ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.2</span></a>, fill=blue!30]
]
]
[Thought Generation <a href="#Ch2.S2.SS3" title="2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3</span></a>, fill=green!50
[Chain-of-Thought
<br class="ltx_break">(CoT) <a href="#Ch2.S2.SS3.SSS0.Px1" title="Chain-of-Thought (CoT) Prompting ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3</span></a>, fill=green!40
[Zero-Shot CoT <a href="#Ch2.S2.SS3.SSS1" title="2.2.3.1 Zero-Shot-CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.1</span></a>, fill=green!30
[Analogical Prompting
<br class="ltx_break"><a href="#Ch2.S2.SS3.SSS1.Px2" title="Analogical Prompting ‣ 2.2.3.1 Zero-Shot-CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.1</span></a>, fill=green!20]
[Step-Back Prompting
<br class="ltx_break"><a href="#Ch2.S2.SS3.SSS1.Px1" title="Step-Back Prompting ‣ 2.2.3.1 Zero-Shot-CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.1</span></a>, fill=green!20]
[Thread-of-Thought
<br class="ltx_break">(ThoT) <a href="#Ch2.S2.SS3.SSS1.Px3" title="Thread-of-Thought (ThoT) Prompting ‣ 2.2.3.1 Zero-Shot-CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.1</span></a>, fill=green!20]
[Tab-CoT <a href="#Ch2.S2.SS3.SSS1.Px4" title="Tabular Chain-of-Thought (Tab-CoT) ‣ 2.2.3.1 Zero-Shot-CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.1</span></a>, fill=green!20]
]
[Few-Shot CoT <a href="#Ch2.S2.SS3.SSS2" title="2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!30
[Active-Prompt <a href="#Ch2.S2.SS3.SSS2.Px4" title="Active Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Auto-CoT <a href="#Ch2.S2.SS3.SSS2.Px6" title="Automatic Chain-of-Thought (Auto-CoT) Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Complexity-Based <a href="#Ch2.S2.SS3.SSS2.Px3" title="Complexity-based Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Contrastive <a href="#Ch2.S2.SS3.SSS2.Px1" title="Contrastive CoT Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Memory-of-Thought
<br class="ltx_break"><a href="#Ch2.S2.SS3.SSS2.Px5" title="Memory-of-Thought Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Uncertainty-Routed
<br class="ltx_break">CoT <a href="#Ch2.S2.SS3.SSS2.Px2" title="Uncertainty-Routed CoT Prompting ‣ 2.2.3.2 Few-Shot CoT ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3.2</span></a>, fill=green!20]
[Prompt Mining <a href="#Ch2.S2.SS1.SSS2.Px4" title="Prompt Mining ‣ 2.2.1.2 Few-Shot Prompting Techniques ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.2</span></a>, fill=green!20]
]
]
]
[Ensembling <a href="#Ch2.S2.SS5" title="2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!50
[COSP <a href="#Ch2.S2.SS5.SSS0.Px8" title="Consistency-based Self-adaptive Prompting (COSP) ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[DENSE <a href="#Ch2.S2.SS5.SSS0.Px1" title="Demonstration Ensembling (DENSE) ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[DiVeRSe <a href="#Ch2.S2.SS5.SSS0.Px7" title="DiVeRSe ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[Max Mutual
<br class="ltx_break">Information <a href="#Ch2.S2.SS5.SSS0.Px3" title="Max Mutual Information Method ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[Meta-CoT <a href="#Ch2.S2.SS5.SSS0.Px6" title="Meta-Reasoning over Multiple CoTs ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[MoRE <a href="#Ch2.S2.SS5.SSS0.Px2" title="Mixture of Reasoning Experts (MoRE) ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[Self-Consistency <a href="#Ch2.S2.SS5.SSS0.Px4" title="Self-Consistency ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[Universal
<br class="ltx_break">Self-Consistency <a href="#Ch2.S2.SS5.SSS0.Px5" title="Universal Self-Consistency ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[USP <a href="#Ch2.S2.SS5.SSS0.Px9" title="Universal Self-Adaptive Prompting (USP) ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
[Prompt Paraphrasing <a href="#Ch2.S2.SS5.SSS0.Px10" title="Prompt Paraphrasing ‣ 2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>, fill=orange!40]
]
[Self-Criticism <a href="#Ch2.S2.SS6" title="2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!50
[Chain-of-Verification <a href="#Ch2.S2.SS6.SSS0.Px5" title="Chain-of-Verification (COVE) ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
[Self-Calibration <a href="#Ch2.S2.SS6.SSS0.Px1" title="Self-Calibration ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
[Self-Refine <a href="#Ch2.S2.SS6.SSS0.Px2" title="Self-Refine ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
[Self-Verification <a href="#Ch2.S2.SS6.SSS0.Px4" title="Self-Verification ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
[ReverseCoT <a href="#Ch2.S2.SS6.SSS0.Px3" title="Reversing Chain-of-Thought (RCoT) ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
[Cumulative Reason. <a href="#Ch2.S2.SS6.SSS0.Px6" title="Cumulative Reasoning ‣ 2.2.6 Self-Criticism ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.6</span></a>, fill=purple!40]
]
[Decomposition <a href="#Ch2.S2.SS4" title="2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!50
[DECOMP <a href="#Ch2.S2.SS4.SSS0.Px2" title="Decomposed Prompting (DECOMP) ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Faithful CoT <a href="#Ch2.S2.SS4.SSS0.Px7" title="Faithful Chain-of-Thought ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Least-to-Most <a href="#Ch2.S2.SS4.SSS0.Px1" title="Least-to-Most Prompting ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Plan-and-Solve <a href="#Ch2.S2.SS4.SSS0.Px3" title="Plan-and-Solve Prompting ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Program-of-Thought <a href="#Ch2.S2.SS4.SSS0.Px6" title="Program-of-Thoughts ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Recurs.-of-Thought <a href="#Ch2.S2.SS4.SSS0.Px5" title="Recursion-of-Thought ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Skeleton-of-Thought <a href="#Ch2.S2.SS4.SSS0.Px8" title="Skeleton-of-Thought ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
[Tree-of-Thought <a href="#Ch2.S2.SS4.SSS0.Px4" title="Tree-of-Thought (ToT) ‣ 2.2.4 Decomposition ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.4</span></a>, fill=brown!40]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.2: </span>All text-based prompting techniques from our dataset.</figcaption>
</figure>
<section id="Ch2.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.1 </span>In-Context Learning (ICL)</h4>

<figure id="Ch2.F3" class="ltx_figure"><img src="/html/2406.06608/assets/x4.png" id="Ch2.F3.g1" class="ltx_graphics ltx_centering ltx_img_landscape" width="461" height="101" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.3: </span>We highlight six main design decisions when crafting few-shot prompts. <sup id="Ch2.F3.5.1" class="ltx_sup">∗</sup>Please note that recommendations here <span id="Ch2.F3.6.2" class="ltx_text ltx_font_italic">do not</span> generalize to all tasks; in some cases, each of them could hurt performance.</figcaption>
</figure>
<div id="Ch2.S2.SS1.p1" class="ltx_para">
<p id="Ch2.S2.SS1.p1.1" class="ltx_p">ICL refers to the ability of GenAIs to learn skills and tasks by providing them with exemplars and or relevant instructions within the prompt, without the need for weight updates/retraining <cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>); Radford et al. (<a href="#bib.bib229" title="" class="ltx_ref">2019b</a>)</cite>. These skills can be learned from exemplars (Figure <a href="#Ch2.F4" title="Figure 2.4 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.4</span></a>) and/or instructions (Figure <a href="#Ch2.F5" title="Figure 2.5 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5</span></a>). Note that the word ’learn’ is misleading. ICL can simply be task specification–the skills are not necessarily new, and can have already been included in the training data (Figure <a href="#Ch2.F6" title="Figure 2.6 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.6</span></a>). See Appendix <a href="#A1.S8" title="A.8 In-Context Learning Definitions Disambiguation ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.8</span></a> for a discussion of the use of this term. Significant work is currently being done on optimizing <cite class="ltx_cite ltx_citemacro_cite">Bansal et al. (<a href="#bib.bib15" title="" class="ltx_ref">2023</a>)</cite> and understanding <cite class="ltx_cite ltx_citemacro_cite">Si et al. (<a href="#bib.bib263" title="" class="ltx_ref">2023a</a>); Štefánik and Kadlčík (<a href="#bib.bib272" title="" class="ltx_ref">2023</a>)</cite> ICL.</p>
</div>
<figure id="Ch2.F4" class="ltx_figure"><svg id="Ch2.F4.pic1" class="ltx_picture ltx_centering" height="70.84" overflow="visible" version="1.1" width="600"><g transform="translate(0,70.84) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 70.84 L 600 70.84 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 68.87 L 598.03 68.87 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="43.28" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.F4.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.F4.pic1.1.1.1.1.1.1" class="ltx_p">2+2: four</span>
<span id="Ch2.F4.pic1.1.1.1.1.1.2" class="ltx_p">4+5: nine</span>
<span id="Ch2.F4.pic1.1.1.1.1.1.3" class="ltx_p">8+0:</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.4: </span>ICL exemplar prompt</figcaption>
</figure>
<figure id="Ch2.F5" class="ltx_figure"><svg id="Ch2.F5.pic1" class="ltx_picture ltx_centering" height="58" overflow="visible" version="1.1" width="600"><g transform="translate(0,58) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 58 L 600 58 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 56.03 L 598.03 56.03 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="30.44" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.F5.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.F5.pic1.1.1.1.1.1.1" class="ltx_p">Extract all words that have 3 of the same letter and at least 3 other letter from the following text: {TEXT}</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.5: </span>ICL instruction prompt</figcaption>
</figure>
<figure id="Ch2.F6" class="ltx_figure"><svg id="Ch2.F6.pic1" class="ltx_picture ltx_centering" height="40.01" overflow="visible" version="1.1" width="600"><g transform="translate(0,40.01) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 40.01 L 600 40.01 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 38.04 L 598.03 38.04 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.45" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.F6.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.F6.pic1.1.1.1.1.1.1" class="ltx_p">Translate the word "cheese" to French.</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.6: </span>ICL from training data prompt. In this version of ICL, the model is not learning a new skill, but rather using knowledge likely in its training set.</figcaption>
</figure>
<section id="Ch2.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Few-Shot Prompting</h6>

<div id="Ch2.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite> is the paradigm seen in Figure <a href="#Ch2.F4" title="Figure 2.4 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.4</span></a>, where the GenAI learns to complete a task with only a few examples (exemplars).</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Few-Shot Learning (FSL) </h6>

<div id="Ch2.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Fei-Fei et al. (<a href="#bib.bib55" title="" class="ltx_ref">2006</a>); Wang et al. (<a href="#bib.bib295" title="" class="ltx_ref">2019</a>)</cite> is often conflated with Few-Shot Prompting <cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>. It is important to note that FSL is a broader machine learning paradigm to adapt parameters with a few examples, while Few-Shot Prompting is specific to prompts in the GenAI settings and does not involve updating model parameters.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">2.2.1.1 </span>Few-Shot Prompting Design Decisions</h5>

<div id="Ch2.S2.SS1.SSS1.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.p1.1" class="ltx_p">Selecting exemplars for a prompt is a difficult task–performance depends significantly on various factors of the exemplars <cite class="ltx_cite ltx_citemacro_cite">Dong et al. (<a href="#bib.bib50" title="" class="ltx_ref">2023</a>)</cite>, and only a limited number of exemplars fit in the typical LLM’s context window. We highlight six separate design decisions, including the selection and order of exemplars that critically influence the output quality <cite class="ltx_cite ltx_citemacro_cite">Zhao et al. (<a href="#bib.bib345" title="" class="ltx_ref">2021a</a>); Lu et al. (<a href="#bib.bib171" title="" class="ltx_ref">2021</a>); Ye and Durrett (<a href="#bib.bib330" title="" class="ltx_ref">2023</a>)</cite> (Figure <a href="#Ch2.F3" title="Figure 2.3 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.3</span></a>).</p>
</div>
<section id="Ch2.S2.SS1.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Quantity</h6>

<div id="Ch2.S2.SS1.SSS1.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px1.p1.1" class="ltx_p">Increasing the quantity of exemplars in the prompt generally improves model performance, particularly in larger models <cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>. However, in some cases, the benefits may diminish beyond 20 exemplars <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib157" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Ordering</h6>

<div id="Ch2.S2.SS1.SSS1.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px2.p1.1" class="ltx_p">The order of exemplars affects model behavior <cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib171" title="" class="ltx_ref">2021</a>); Kumar and Talukdar (<a href="#bib.bib128" title="" class="ltx_ref">2021</a>); Liu et al. (<a href="#bib.bib157" title="" class="ltx_ref">2021</a>); Rubin et al. (<a href="#bib.bib236" title="" class="ltx_ref">2022</a>)</cite>. On some tasks, exemplar order can cause accuracy to vary from sub-50% to 90%+ <cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib171" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Label Distribution</h6>

<div id="Ch2.S2.SS1.SSS1.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px3.p1.1" class="ltx_p">As in traditional supervised machine learning, the distribution of exemplar labels in the prompt affects behavior. For example, if 10 exemplars from one class and 2 exemplars of another class are included, this may cause the model to be biased toward the first class.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Label Quality</h6>

<div id="Ch2.S2.SS1.SSS1.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px4.p1.1" class="ltx_p">Despite the general benefit of multiple exemplars, the necessity of strictly <em id="Ch2.S2.SS1.SSS1.Px4.p1.1.1" class="ltx_emph ltx_font_italic">valid</em> demonstrations is unclear. Some work <cite class="ltx_cite ltx_citemacro_cite">Min et al. (<a href="#bib.bib184" title="" class="ltx_ref">2022</a>)</cite> suggests that the accuracy of labels is irrelevant—providing models with exemplars with incorrect labels may not negatively diminish performance.
However, under certain settings, there is a significant impact on performance <cite class="ltx_cite ltx_citemacro_cite">Yoo et al. (<a href="#bib.bib331" title="" class="ltx_ref">2022</a>)</cite>. Larger models are often better at handling incorrect or unrelated labels <cite class="ltx_cite ltx_citemacro_cite">Wei et al. (<a href="#bib.bib302" title="" class="ltx_ref">2023c</a>)</cite>.</p>
</div>
<div id="Ch2.S2.SS1.SSS1.Px4.p2" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px4.p2.1" class="ltx_p">It is important to discuss this factor, since if you are automatically constructing prompts from large datasets that may contain inaccuracies, it may be necessary to study how label quality affects your results.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Format</h6>

<div id="Ch2.S2.SS1.SSS1.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px5.p1.1" class="ltx_p">The formatting of exemplars also affects performance. One of the most common formats is “Q: {input}, A: {label}”, but the optimal format may vary across tasks; it may be worth trying multiple formats to see which performs best. There is some evidence to suggest that formats that occur commonly in the training data will lead to better performance <cite class="ltx_cite ltx_citemacro_cite">Jiang et al. (<a href="#bib.bib106" title="" class="ltx_ref">2020</a>)</cite>.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS1.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Exemplar Similarity</h6>

<div id="Ch2.S2.SS1.SSS1.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS1.Px6.p1.1" class="ltx_p">Selecting exemplars that are similar to the test sample is generally beneficial for performance <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib157" title="" class="ltx_ref">2021</a>); Min et al. (<a href="#bib.bib184" title="" class="ltx_ref">2022</a>)</cite>. However, in some cases, selecting more diverse exemplars can improve performance <cite class="ltx_cite ltx_citemacro_cite">Su et al. (<a href="#bib.bib273" title="" class="ltx_ref">2022</a>); Min et al. (<a href="#bib.bib184" title="" class="ltx_ref">2022</a>)</cite>.</p>
</div>
</section>
</section>
<section id="Ch2.S2.SS1.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">2.2.1.2 </span>Few-Shot Prompting Techniques</h5>

<div id="Ch2.S2.SS1.SSS2.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.p1.1" class="ltx_p">Considering all of these factors, Few-Shot Prompting can be very difficult to implement effectively. We now examine techniques for Few-Shot Prompting in the supervised setting. Ensembling approaches can also benefit Few-Shot Prompting, but we discuss them separately (Section <a href="#Ch2.S2.SS5" title="2.2.5 Ensembling ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.5</span></a>).</p>
</div>
<div id="Ch2.S2.SS1.SSS2.p2" class="ltx_para">
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encoding="application/x-tex" id="Ch2.S2.SS1.SSS2.p2.2.m2.1c">D^{train}_{x^{i}}</annotation></semantics></math> and outputs <math id="Ch2.S2.SS1.SSS2.p2.3.m3.1" class="ltx_Math" alttext="D^{train}_{y^{i}}" display="inline"><semantics id="Ch2.S2.SS1.SSS2.p2.3.m3.1a"><msubsup id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.2" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.2.cmml">D</mi><msup id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.2" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.2.cmml">y</mi><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.3" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.3.cmml">i</mi></msup><mrow id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.2" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.2.cmml">t</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.3" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.3.cmml">r</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1a" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.4" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.4.cmml">a</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1b" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.5" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.5.cmml">i</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1c" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.6" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.6.cmml">n</mi></mrow></msubsup><annotation-xml encoding="MathML-Content" id="Ch2.S2.SS1.SSS2.p2.3.m3.1b"><apply id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1">subscript</csymbol><apply id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.2">𝐷</ci><apply id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3"><times id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.1"></times><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.2">𝑡</ci><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.3">𝑟</ci><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.4.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.4">𝑎</ci><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.5.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.5">𝑖</ci><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.6.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.2.3.6">𝑛</ci></apply></apply><apply id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.2">𝑦</ci><ci id="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.3.m3.1.1.3.3">𝑖</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S2.SS1.SSS2.p2.3.m3.1c">D^{train}_{y^{i}}</annotation></semantics></math>, which can be used to few-shot prompt a GenAI (rather than performing gradient-based updates). Assume that this prompt can be dynamically generated with respect to <math id="Ch2.S2.SS1.SSS2.p2.4.m4.1" class="ltx_Math" alttext="D^{test}_{x^{i}}" display="inline"><semantics id="Ch2.S2.SS1.SSS2.p2.4.m4.1a"><msubsup id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.2" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.2.cmml">D</mi><msup id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.2" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.2.cmml">x</mi><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.3" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.3.cmml">i</mi></msup><mrow id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.cmml"><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.2" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.2.cmml">t</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.3" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.3.cmml">e</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1a" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.4" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.4.cmml">s</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1b" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.5" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.5.cmml">t</mi></mrow></msubsup><annotation-xml encoding="MathML-Content" id="Ch2.S2.SS1.SSS2.p2.4.m4.1b"><apply id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1">subscript</csymbol><apply id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.2">𝐷</ci><apply id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3"><times id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.1"></times><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.2">𝑡</ci><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.3">𝑒</ci><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.4.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.4">𝑠</ci><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.5.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.2.3.5">𝑡</ci></apply></apply><apply id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.1.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.2.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.2">𝑥</ci><ci id="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.3.cmml" xref="Ch2.S2.SS1.SSS2.p2.4.m4.1.1.3.3">𝑖</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S2.SS1.SSS2.p2.4.m4.1c">D^{test}_{x^{i}}</annotation></semantics></math> at test time. Here is the prompt template we will use for this section, following the ‘input: output‘ format (Figure <a href="#Ch2.F4" title="Figure 2.4 ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.4</span></a>):</p>
</div>
<figure id="Ch2.F7" class="ltx_figure"><svg id="Ch2.F7.pic1" class="ltx_picture ltx_centering" height="58.4" overflow="visible" version="1.1" width="600"><g transform="translate(0,58.4) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 58.4 L 600 58.4 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 56.43 L 598.03 56.43 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="30.84" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.2" class="ltx_p">{Exemplars}</span>
<span id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_p"><math id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="D^{test}_{x^{i}}" display="inline"><semantics id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1a"><msubsup id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml"><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.2" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.2.cmml">D</mi><msup id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml"><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2.cmml">x</mi><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3.cmml">i</mi></msup><mrow id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.cmml"><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.2" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.2.cmml">t</mi><mo lspace="0em" rspace="0em" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.3" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.3.cmml">e</mi><mo lspace="0em" rspace="0em" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1a" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.4" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.4.cmml">s</mi><mo lspace="0em" rspace="0em" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1b" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.5" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.5.cmml">t</mi></mrow></msubsup><annotation-xml encoding="MathML-Content" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1b"><apply id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1">subscript</csymbol><apply id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.1.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1">superscript</csymbol><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.2.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.2">𝐷</ci><apply id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3"><times id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.1"></times><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.2.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.2">𝑡</ci><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.3.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.3">𝑒</ci><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.4.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.4">𝑠</ci><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.5.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.3.5">𝑡</ci></apply></apply><apply id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3"><csymbol cd="ambiguous" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3">superscript</csymbol><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2">𝑥</ci><ci id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3.cmml" xref="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3">𝑖</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch2.F7.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1c">D^{test}_{x^{i}}</annotation></semantics></math>:</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.7: </span>Few-Shot Prompting Template</figcaption>
</figure>
<section id="Ch2.S2.SS1.SSS2.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">K-Nearest Neighbor (KNN)</h6>

<div id="Ch2.S2.SS1.SSS2.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib157" title="" class="ltx_ref">2021</a>)</cite>

is part of a family of algorithms that selects exemplars similar to <math id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1" class="ltx_Math" alttext="D^{test}_{x^{i}}" display="inline"><semantics id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1a"><msubsup id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.cmml"><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.2" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.2.cmml">D</mi><msup id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.cmml"><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.2" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.2.cmml">x</mi><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.3" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.3.cmml">i</mi></msup><mrow id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.cmml"><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.2" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.2.cmml">t</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.3" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.3.cmml">e</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1a" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.4" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.4.cmml">s</mi><mo lspace="0em" rspace="0em" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1b" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1.cmml">​</mo><mi id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.5" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.5.cmml">t</mi></mrow></msubsup><annotation-xml encoding="MathML-Content" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1b"><apply id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.1.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1">subscript</csymbol><apply id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.1.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.2.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.2">𝐷</ci><apply id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3"><times id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.1"></times><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.2.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.2">𝑡</ci><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.3.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.3">𝑒</ci><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.4.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.4">𝑠</ci><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.5.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.2.3.5">𝑡</ci></apply></apply><apply id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3"><csymbol cd="ambiguous" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.1.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3">superscript</csymbol><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.2.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.2">𝑥</ci><ci id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.3.cmml" xref="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1.1.3.3">𝑖</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch2.S2.SS1.SSS2.Px1.p1.1.m1.1c">D^{test}_{x^{i}}</annotation></semantics></math> to boost performance. Although effective, employing KNN during prompt generation may be time and resource intensive.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS2.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Vote-K</h6>

<div id="Ch2.S2.SS1.SSS2.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Su et al. (<a href="#bib.bib273" title="" class="ltx_ref">2022</a>)</cite>  is another method to select similar exemplars to the test sample. In one stage, a model proposes useful unlabeled candidate exemplars for an annotator to label. In the second stage, the labeled pool is used for Few-Shot Prompting. Vote-K also ensures that newly added exemplars are sufficiently different than existing ones to increase diversity and representativeness.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS2.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_font_italic ltx_title_paragraph">Self-Generated In-Context Learning (SG-ICL)</h6>

<div id="Ch2.S2.SS1.SSS2.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Kim et al. (<a href="#bib.bib121" title="" class="ltx_ref">2022</a>)</cite> leverages a GenAI to automatically generate exemplars. While better than zero-shot scenarios when training data is unavailable, the generated samples are not as effective as actual data.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS2.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Mining</h6>

<div id="Ch2.S2.SS1.SSS2.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Jiang et al. (<a href="#bib.bib106" title="" class="ltx_ref">2020</a>)</cite>  is the process of
discovering optimal "middle words" in prompts (effectively prompt templates) through large corpus analysis. For example, instead of using the common "Q: A:" format for few-shot prompts, there may exist something similar which occurs more frequently in the corpus. Formats which occur more often in the corpus will likely lead to improved prompt performance.</p>
</div>
</section>
<section id="Ch2.S2.SS1.SSS2.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">More Complicated Techniques</h6>

<div id="Ch2.S2.SS1.SSS2.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS1.SSS2.Px5.p1.1" class="ltx_p">such as LENS <cite class="ltx_cite ltx_citemacro_cite">Li and Qiu (<a href="#bib.bib145" title="" class="ltx_ref">2023a</a>)</cite>, UDR <cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib144" title="" class="ltx_ref">2023f</a>)</cite>, and Active Example Selection <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib340" title="" class="ltx_ref">2022a</a>)</cite> leverage iterative filtering, embedding and retrieval, and reinforcement learning, respectively.</p>
</div>
</section>
</section>
</section>
<section id="Ch2.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.2 </span>Zero-Shot</h4>

<div id="Ch2.S2.SS2.p1" class="ltx_para">
<p id="Ch2.S2.SS2.p1.1" class="ltx_p">In contrast to Few-Shot Prompting, Zero-Shot Prompting uses zero exemplars. There are a number of well known standalone zero-shot techniques as well as zero-shot techniques combine with another concept (e.g. Chain of Thought), which we discuss later (Section <a href="#Ch2.S2.SS3" title="2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.3</span></a>).</p>
</div>
<section id="Ch2.S2.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Role Prompting</h6>

<div id="Ch2.S2.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib296" title="" class="ltx_ref">2023j</a>); Zheng et al. (<a href="#bib.bib352" title="" class="ltx_ref">2023d</a>)</cite>
, also known as persona prompting <cite class="ltx_cite ltx_citemacro_cite">Schmidt et al. (<a href="#bib.bib245" title="" class="ltx_ref">2023</a>); Wang et al. (<a href="#bib.bib298" title="" class="ltx_ref">2023l</a>)</cite>, assigns a specific role to the GenAI in the prompt. For example, the user might prompt it to act like "Madonna" or a "travel writer". This can create more desirable outputs for open-ended tasks <cite class="ltx_cite ltx_citemacro_cite">Reynolds and McDonell (<a href="#bib.bib233" title="" class="ltx_ref">2021</a>)</cite> and in some cases improve accuracy on benchmarks <cite class="ltx_cite ltx_citemacro_cite">Zheng et al. (<a href="#bib.bib352" title="" class="ltx_ref">2023d</a>)</cite>.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Style Prompting</h6>

<div id="Ch2.S2.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib168" title="" class="ltx_ref">2023a</a>)</cite>

involves specifying the desired style, tone, or genre in the prompt to shape the output of a GenAI. A similar effect can be achieved using role prompting.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Emotion Prompting</h6>

<div id="Ch2.S2.SS2.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib138" title="" class="ltx_ref">2023a</a>)</cite>
 incorporates phrases of psychological relevance to humans (e.g., "This is important to my career") into the prompt, which may lead to improved LLM performance on benchmarks and open-ended text generation.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">System 2 Attention (S2A)</h6>

<div id="Ch2.S2.SS2.SSS0.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Weston and Sukhbaatar (<a href="#bib.bib304" title="" class="ltx_ref">2023</a>)</cite> first asks an LLM to rewrite the prompt and remove any information unrelated to the question therein. Then, it passes this new prompt into an LLM to retrieve a final response.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">SimToM</h6>

<div id="Ch2.S2.SS2.SSS0.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wilf et al. (<a href="#bib.bib306" title="" class="ltx_ref">2023</a>)</cite> deals with complicated questions which involve multiple people or objects. Given the question, it attempts to establish the set of facts one person knows, then answer the question based only on those facts. This is a two prompt process and can help eliminate the effect of irrelevant information in the prompt.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Rephrase and Respond (RaR)</h6>

<div id="Ch2.S2.SS2.SSS0.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Deng et al. (<a href="#bib.bib46" title="" class="ltx_ref">2023</a>)</cite>
instructs the LLM to rephrase and expand the question before generating the final answer. For example, it might add the following phrase to the question: "Rephrase and expand the question, and respond". This could all be done in a single pass or the new question could be passed to the LLM separately. RaR has demonstrated improvements on multiple benchmarks.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px7" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Re-reading (RE2)</h6>

<div id="Ch2.S2.SS2.SSS0.Px7.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px7.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Xu et al. (<a href="#bib.bib317" title="" class="ltx_ref">2023</a>)</cite>
adds the phrase "Read the question again:" to the prompt in addition to repeating the question. Although this is such a simple technique, it has shown improvement in reasoning benchmarks, especially with complex questions.</p>
</div>
</section>
<section id="Ch2.S2.SS2.SSS0.Px8" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Ask</h6>

<div id="Ch2.S2.SS2.SSS0.Px8.p1" class="ltx_para">
<p id="Ch2.S2.SS2.SSS0.Px8.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Press et al. (<a href="#bib.bib220" title="" class="ltx_ref">2022</a>)</cite> 
prompts LLMs to first decide if they need to ask follow up questions for a given prompt. If so, the LLM generates these questions, then answers them and finally answers the original question.</p>
</div>
</section>
</section>
<section id="Ch2.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.3 </span>Thought Generation</h4>

<div id="Ch2.S2.SS3.p1" class="ltx_para">
<p id="Ch2.S2.SS3.p1.1" class="ltx_p">Thought generation encompasses a range of techniques that prompt the LLM to articulate its reasoning while solving a problem <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib341" title="" class="ltx_ref">2023c</a>)</cite>.</p>
</div>
<section id="Ch2.S2.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Chain-of-Thought (CoT) Prompting</h6>

<div id="Ch2.S2.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wei et al. (<a href="#bib.bib299" title="" class="ltx_ref">2022</a>)</cite> 
leverages few-shot prompting to encourage the LLM to express its thought process before delivering its final answer.<span id="Ch2.footnote5" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">5</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">5</sup><span class="ltx_tag ltx_tag_note">5</span>We note that such techniques are often described using words like ”think” that anthropomorphize models. We attempt not to use this language, but do use original authors’ language where appropriate.</span></span></span> This technique is occasionally referred to as Chain-of-Thoughts <cite class="ltx_cite ltx_citemacro_cite">Tutunov et al. (<a href="#bib.bib281" title="" class="ltx_ref">2023</a>); Besta et al. (<a href="#bib.bib17" title="" class="ltx_ref">2024</a>); Chen et al. (<a href="#bib.bib32" title="" class="ltx_ref">2023d</a>)</cite>. It has been demonstrated to significantly enhance the LLM’s performance in mathematics and reasoning tasks. In <cite class="ltx_cite ltx_citemacro_citet">Wei et al. (<a href="#bib.bib299" title="" class="ltx_ref">2022</a>)</cite>, the prompt includes an exemplar featuring a question, a reasoning path, and the correct answer (Figure <a href="#Ch2.F8" title="Figure 2.8 ‣ Chain-of-Thought (CoT) Prompting ‣ 2.2.3 Thought Generation ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.8</span></a>).</p>
</div>
<figure id="Ch2.F8" class="ltx_figure"><svg id="Ch2.F8.pic1" class="ltx_picture ltx_centering" height="103.43" overflow="visible" version="1.1" width="600"><g transform="translate(0,103.43) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 103.43 L 600 103.43 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 101.46 L 598.03 101.46 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="75.87" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.F8.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.F8.pic1.1.1.1.1.1.1" class="ltx_p">Q: Jack has two baskets, each containing three balls. How many balls does Jack have in total?</span>
<span id="Ch2.F8.pic1.1.1.1.1.1.2" class="ltx_p">A: One basket contains 3 balls, so two baskets contain 3 * 2 = 6 balls.</span>
<span id="Ch2.F8.pic1.1.1.1.1.1.3" class="ltx_p">Q: {QUESTION}</span>
<span id="Ch2.F8.pic1.1.1.1.1.1.4" class="ltx_p">A:</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.8: </span>A One-Shot Chain-of-Thought Prompt.</figcaption>
</figure>
</section>
<section id="Ch2.S2.SS3.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">2.2.3.1 </span>Zero-Shot-CoT</h5>

<div id="Ch2.S2.SS3.SSS1.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS1.p1.1" class="ltx_p">The most straightforward version of CoT contains zero exemplars. It involves appending a thought inducing phrase like "Let’s think step by step." <cite class="ltx_cite ltx_citemacro_cite">Kojima et al. (<a href="#bib.bib127" title="" class="ltx_ref">2022</a>)</cite> to the prompt. Other suggested thought-generating phrases include "Let’s work this out in a step by step way to be sure we have the right answer" <cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib354" title="" class="ltx_ref">2022b</a>)</cite> and "First, let’s think about this logically" <cite class="ltx_cite ltx_citemacro_cite">Kojima et al. (<a href="#bib.bib127" title="" class="ltx_ref">2022</a>)</cite>. <cite class="ltx_cite ltx_citemacro_citet">Yang et al. (<a href="#bib.bib319" title="" class="ltx_ref">2023a</a>)</cite> searches for an optimal thought inducer. Zero-Shot-CoT approaches are attractive as they don’t require exemplars and are generally task agnostic.</p>
</div>
<section id="Ch2.S2.SS3.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Step-Back Prompting</h6>

<div id="Ch2.S2.SS3.SSS1.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS1.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zheng et al. (<a href="#bib.bib351" title="" class="ltx_ref">2023c</a>)</cite> 
is a modification of CoT where the LLM is first asked a generic, high-level question about relevant concepts or facts before delving into reasoning. This approach has improved performance significantly on multiple reasoning benchmarks for both PaLM-2L and GPT-4.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Analogical Prompting</h6>

<div id="Ch2.S2.SS3.SSS1.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS1.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yasunaga et al. (<a href="#bib.bib328" title="" class="ltx_ref">2023</a>)</cite> 
is similar to SG-ICL, and automatically generates exemplars that include CoTs. It has demonstrated improvements in mathematical reasoning and code generation tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS1.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Thread-of-Thought (ThoT) Prompting</h6>

<div id="Ch2.S2.SS3.SSS1.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS1.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib355" title="" class="ltx_ref">2023</a>)</cite> consists of an improved thought inducer for CoT reasoning. Instead of "Let’s think step by step," it uses "Walk me through this context in manageable parts step by step, summarizing and analyzing as we go." This thought inducer works well in question-answering and retrieval settings, especially when dealing with large, complex contexts.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS1.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Tabular Chain-of-Thought (Tab-CoT)</h6>

<div id="Ch2.S2.SS3.SSS1.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS1.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Jin and Lu (<a href="#bib.bib108" title="" class="ltx_ref">2023</a>)</cite> 
consists of a Zero-Shot CoT prompt that makes the LLM output reasoning as a markdown table. This tabular design enables the LLM to improve the structure and thus the reasoning of its output.</p>
</div>
</section>
</section>
<section id="Ch2.S2.SS3.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">2.2.3.2 </span>Few-Shot CoT</h5>

<div id="Ch2.S2.SS3.SSS2.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.p1.1" class="ltx_p">This set of techniques present the LLM with multiple exemplars, which include chains-of-thought. This can significantly enhance performance. This technique is occasionally referred to as Manual-CoT <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib342" title="" class="ltx_ref">2022b</a>)</cite> or Golden CoT <cite class="ltx_cite ltx_citemacro_cite">Del and Fishel (<a href="#bib.bib44" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
<section id="Ch2.S2.SS3.SSS2.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Contrastive CoT Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chia et al. (<a href="#bib.bib37" title="" class="ltx_ref">2023</a>)</cite>  adds both exemplars with incorrect and correct explanations to the CoT prompt in order to show the LLM how <span id="Ch2.S2.SS3.SSS2.Px1.p1.1.1" class="ltx_text ltx_font_italic">not</span> to reason. This method has shown significant improvement in areas like Arithmetic Reasoning and Factual QA.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS2.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Uncertainty-Routed CoT Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Google (<a href="#bib.bib76" title="" class="ltx_ref">2023</a>)</cite> 
samples multiple CoT reasoning paths, then selects the majority if it is above a certain threshold (calculated based on validation data). If not, it samples greedily and selects that response. This method demonstrates improvement on the MMLU benchmark for both GPT4 and Gemini Ultra models.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS2.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Complexity-based Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Fu et al. (<a href="#bib.bib60" title="" class="ltx_ref">2023b</a>)</cite> involves two major modifications to CoT. First, it selects complex examples for annotation and inclusion in the prompt, based on factors like question length or reasoning steps required. Second, during inference, it samples multiple reasoning chains (answers) and uses a majority vote among chains exceeding a certain length threshold, under the premise that longer reasoning indicates higher answer quality. This technique has shown improvements on three mathematical reasoning datasets.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS2.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Active Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Diao et al. (<a href="#bib.bib48" title="" class="ltx_ref">2023</a>)</cite>  starts with some training questions/exemplars, asks the LLM to solve them, then calculates uncertainty (disagreement in this case) and asks human annotators to rewrite the exemplars with highest uncertainty.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS2.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Memory-of-Thought Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Li and Qiu (<a href="#bib.bib146" title="" class="ltx_ref">2023b</a>)</cite>  leverage unlabeled training exemplars to build Few-Shot CoT prompts at test time. Before test time, it performs inference on the unlabeled training exemplars with CoT. At test time, it retrieves similar instances to the test sample. This technique has shown substantial improvements in benchmarks like Arithmetic, commonsense, and factual reasoning.</p>
</div>
</section>
<section id="Ch2.S2.SS3.SSS2.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Automatic Chain-of-Thought (Auto-CoT) Prompting</h6>

<div id="Ch2.S2.SS3.SSS2.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS3.SSS2.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib342" title="" class="ltx_ref">2022b</a>)</cite> uses <cite class="ltx_cite ltx_citemacro_citet">Wei et al. (<a href="#bib.bib299" title="" class="ltx_ref">2022</a>)</cite>’s Zero-Shot prompt to automatically generate chains of thought. These are then used to build a Few-Shot CoT prompt for a test sample.</p>
</div>
</section>
</section>
</section>
<section id="Ch2.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.4 </span>Decomposition</h4>

<div id="Ch2.S2.SS4.p1" class="ltx_para">
<p id="Ch2.S2.SS4.p1.1" class="ltx_p">Significant research has focused on decomposing complex problems into simpler sub-questions. This is an effective problem-solving strategy for humans as well as GenAI <cite class="ltx_cite ltx_citemacro_cite">Patel et al. (<a href="#bib.bib201" title="" class="ltx_ref">2022</a>)</cite>. Some decomposition techniques are similar to thought-inducing techniques, such as CoT, which often naturally breaks down problems into simpler components. However, explicitly breaking down problems can further improve LLMs’ problem solving ability.</p>
</div>
<section id="Ch2.S2.SS4.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Least-to-Most Prompting</h6>

<div id="Ch2.S2.SS4.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib353" title="" class="ltx_ref">2022a</a>)</cite> 
starts by prompting a LLM to break a given problem into sub-problems without solving them. Then, it solves them sequentially, appending model responses to the prompt each time, until it arrives at a final result. This method has shown significant improvements in tasks involving symbolic manipulation, compositional generalization, and mathematical reasoning.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Decomposed Prompting (DECOMP)</h6>

<div id="Ch2.S2.SS4.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Khot et al. (<a href="#bib.bib118" title="" class="ltx_ref">2022</a>)</cite> 
Few-Shot prompts a LLM to show it how to use certain functions. These might include things like string splitting or internet searching; these are often implemented as separate LLM calls. Given this, the LLM breaks down its original problem into sub-problems which it sends to different functions. It has shown improved performance over Least-to-Most prompting on some tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Plan-and-Solve Prompting</h6>

<div id="Ch2.S2.SS4.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib290" title="" class="ltx_ref">2023f</a>)</cite> consists of an improved Zero-Shot CoT prompt, "Let’s first understand the problem and devise a plan to solve it. Then, let’s carry out the plan and solve the problem step by step". This method generates more robust reasoning processes than standard Zero-Shot-CoT on multiple reasoning datasets.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Tree-of-Thought (ToT)</h6>

<div id="Ch2.S2.SS4.SSS0.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yao et al. (<a href="#bib.bib325" title="" class="ltx_ref">2023b</a>)</cite>, also known as Tree of Thoughts, <cite class="ltx_cite ltx_citemacro_cite">Long (<a href="#bib.bib166" title="" class="ltx_ref">2023</a>)</cite>, creates a tree-like search problem by starting with an initial problem then generating multiple possible steps in the form of thoughts (as from a CoT). It evaluates the progress each step makes towards solving the problem (through prompting) and decides which steps to continue with, then keeps creating more thoughts. ToT is particularly effective for tasks that require search and planning.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Recursion-of-Thought</h6>

<div id="Ch2.S2.SS4.SSS0.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lee and Kim (<a href="#bib.bib133" title="" class="ltx_ref">2023</a>)</cite>  is similar to regular CoT. However, every time it encounters a complicated problem in the middle of its reasoning chain, it sends this problem into another prompt/LLM call. After this is completed, the answer is inserted into the original prompt. In this way, it can recursively solve complex problems, including ones which might otherwise run over that maximum context length. This method has shown improvements on arithmetic and algorithmic tasks. Though implemented using fine-tuning to output a special token that sends sub-problem into another prompt, it could also be done only through prompting.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Program-of-Thoughts</h6>

<div id="Ch2.S2.SS4.SSS0.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib32" title="" class="ltx_ref">2023d</a>)</cite> 
uses LLMs like Codex to generate programming code as reasoning steps. A code interpreter executes these steps to obtain the final answer. It excels in mathematical and programming-related tasks but is less effective for semantic reasoning tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px7" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Faithful Chain-of-Thought</h6>

<div id="Ch2.S2.SS4.SSS0.Px7.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px7.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lyu et al. (<a href="#bib.bib175" title="" class="ltx_ref">2023</a>)</cite> 
generates a CoT that has both natural language and symbolic language (e.g. Python) reasoning, just like Program-of-Thoughts. However, it also makes use of different types of symbolic languages in a task-dependent fashion.</p>
</div>
</section>
<section id="Ch2.S2.SS4.SSS0.Px8" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Skeleton-of-Thought</h6>

<div id="Ch2.S2.SS4.SSS0.Px8.p1" class="ltx_para">
<p id="Ch2.S2.SS4.SSS0.Px8.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Ning et al. (<a href="#bib.bib195" title="" class="ltx_ref">2023</a>)</cite>  focuses on accelerating answer speed through parallelization. Given a problem, it prompts an LLM to create a skeleton of the answer, in a sense, sub-problems to be solved. Then, in parallel, it sends these questions to an LLM and concatenates all the outputs to get a final response.</p>
</div>
</section>
</section>
<section id="Ch2.S2.SS5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.5 </span>Ensembling</h4>

<div id="Ch2.S2.SS5.p1" class="ltx_para">
<p id="Ch2.S2.SS5.p1.1" class="ltx_p">In GenAI, ensembling is the process of using multiple prompts to solve the same problem, then aggregating these responses into a final output. In many cases, a majority vote—selecting the most frequent response—is used to generate the final output. Ensembling techniques reduce the variance of LLM outputs and often improving accuracy, but come with the cost of increasing the number of model calls needed to reach a final answer.</p>
</div>
<section id="Ch2.S2.SS5.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Demonstration Ensembling (DENSE)</h6>

<div id="Ch2.S2.SS5.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Khalifa et al. (<a href="#bib.bib114" title="" class="ltx_ref">2023</a>)</cite>
creates multiple few-shot prompts, each containing a distinct subset of exemplars from the training set. Next, it aggregates over their outputs to generate a final response.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Mixture of Reasoning Experts (MoRE)</h6>

<div id="Ch2.S2.SS5.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Si et al. (<a href="#bib.bib266" title="" class="ltx_ref">2023d</a>)</cite> creates a set of diverse reasoning experts by using different specialized prompts for different reasoning types (such as retrieval augmentation prompts for factual reasoning, Chain-of-Thought reasoning for multi-hop and math reasoning, and generated knowledge prompting for commonsense reasoning). The best answer from all experts is selected based on an agreement score.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Max Mutual Information Method</h6>

<div id="Ch2.S2.SS5.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Sorensen et al. (<a href="#bib.bib270" title="" class="ltx_ref">2022</a>)</cite>
creates multiple prompt templates with varied styles and exemplars, then selects the optimal template as the one that maximizes mutual information between the prompt and the LLM’s outputs.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Consistency</h6>

<div id="Ch2.S2.SS5.SSS0.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib293" title="" class="ltx_ref">2022</a>)</cite>
 is based on the intuition that multiple different reasoning paths can lead to the same answer. This method first prompts the LLM multiple times to perform CoT, crucially with a non-zero temperature to elicit diverse reasoning paths. Next, it uses a majority vote over all generated responses to select a final response. Self-Consistency has shown improvements on arithmetic, commonsense, and symbolic reasoning tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Universal Self-Consistency</h6>

<div id="Ch2.S2.SS5.SSS0.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib33" title="" class="ltx_ref">2023e</a>)</cite>
is similar to Self-Consistency except that rather that selecting the majority response by programmatically counting how often it occurs, it inserts all outputs into a prompt template that selects the majority answer. This is helpful for free-form text generation and cases where the same answer may be output slightly differently by different prompts.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Meta-Reasoning over Multiple CoTs</h6>

<div id="Ch2.S2.SS5.SSS0.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yoran et al. (<a href="#bib.bib332" title="" class="ltx_ref">2023</a>)</cite> is similar to universal Self-Consistency; it first generates multiple reasoning chains (but not necessarily final answers) for a given problem. Next, it inserts all of these chains in a single prompt template then generates a final answer from them.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px7" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">DiVeRSe</h6>

<div id="Ch2.S2.SS5.SSS0.Px7.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px7.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib150" title="" class="ltx_ref">2023i</a>)</cite> 
creates multiple prompts for a given problem then performs Self-Consistency for each, generating multiple reasoning paths. They score reasoning paths based on each step in them then select a final response.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px8" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Consistency-based Self-adaptive Prompting (COSP)</h6>

<div id="Ch2.S2.SS5.SSS0.Px8.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px8.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wan et al. (<a href="#bib.bib283" title="" class="ltx_ref">2023a</a>)</cite> constructs Few-Shot CoT prompts by running Zero-Shot CoT with Self-Consistency on a set of examples then selecting a high agreement subset of the outputs to be included in the final prompt as exemplars. It again performs Self-Consistency with this final prompt.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px9" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Universal Self-Adaptive Prompting (USP)</h6>

<div id="Ch2.S2.SS5.SSS0.Px9.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px9.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wan et al. (<a href="#bib.bib284" title="" class="ltx_ref">2023b</a>)</cite>
builds upon the success of COSP, aiming to make it generalizable to all tasks. USP makes use of unlabeled data to generate exemplars and a more complicated scoring function to select them. Additionally, USP does not use Self-Consistency.</p>
</div>
</section>
<section id="Ch2.S2.SS5.SSS0.Px10" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Paraphrasing</h6>

<div id="Ch2.S2.SS5.SSS0.Px10.p1" class="ltx_para">
<p id="Ch2.S2.SS5.SSS0.Px10.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Jiang et al. (<a href="#bib.bib106" title="" class="ltx_ref">2020</a>)</cite>  transforms an original prompt by changing some of the wording, while still maintaining the overall meaning. It is effectively a data augmentation technique that can be used to generate prompts for an ensemble.</p>
</div>
</section>
</section>
<section id="Ch2.S2.SS6" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.2.6 </span>Self-Criticism</h4>

<div id="Ch2.S2.SS6.p1" class="ltx_para">
<p id="Ch2.S2.SS6.p1.1" class="ltx_p">When creating GenAI systems, it can be useful to have LLMs criticize their own outputs <cite class="ltx_cite ltx_citemacro_citep">(Huang et al., <a href="#bib.bib98" title="" class="ltx_ref">2022</a>)</cite>. This could simply be a judgement (e.g., is this output correct) or the LLM could be prompted to provide feedback, which is then used to improve the answer. Many approaches to generating and integrating self-criticism have been developed.</p>
</div>
<section id="Ch2.S2.SS6.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Calibration</h6>

<div id="Ch2.S2.SS6.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Kadavath et al. (<a href="#bib.bib109" title="" class="ltx_ref">2022</a>)</cite> first prompts an LLM to answer a question. Then, it builds a new prompt that includes the question, the LLM’s answer, and an additional instruction asking whether the answer is correct. This can be useful for gauging confidence levels when applying LLMs when deciding when to accept or revise the original answer.</p>
</div>
</section>
<section id="Ch2.S2.SS6.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Refine</h6>

<div id="Ch2.S2.SS6.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Madaan et al. (<a href="#bib.bib177" title="" class="ltx_ref">2023</a>)</cite> 
is an iterative framework where, given an initial answer from the LLM, it prompts the same LLM to provide feedback on the answer, and then prompts the LLM to improve the answer based on the feedback.
This iterative process continues until a stopping condition is met (e.g., max number of steps reached). Self-Refine has demonstrated improvement across a range of reasoning, coding, and generation tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS6.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Reversing Chain-of-Thought (RCoT)</h6>

<div id="Ch2.S2.SS6.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Xue et al. (<a href="#bib.bib318" title="" class="ltx_ref">2023</a>)</cite>
first prompts LLMs to reconstruct the problem based on
generated answer. Then, it generates fine-grained comparisons between the original problem
and the reconstructed problem as a way to check for any inconsistencies.
These inconsistencies are then converted to feedback for the LLM to revise the generated answer.</p>
</div>
</section>
<section id="Ch2.S2.SS6.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Verification</h6>

<div id="Ch2.S2.SS6.SSS0.Px4.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Weng et al. (<a href="#bib.bib303" title="" class="ltx_ref">2022</a>)</cite> 
generates multiple candidate solutions with Chain-of-Thought (CoT). It then scores each solution by masking certain parts of the original question and asking an LLM to predict them based on the rest of the question and the generated solution. This method has shown improvement on eight reasoning datasets.</p>
</div>
</section>
<section id="Ch2.S2.SS6.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Chain-of-Verification (COVE)</h6>

<div id="Ch2.S2.SS6.SSS0.Px5.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Dhuliawala et al. (<a href="#bib.bib47" title="" class="ltx_ref">2023</a>)</cite>  first uses an LLM to generate an answer to a given question. Then creates a list of related questions that would help verify the correctness of the answer. Each question is answered by the LLM, then all the information is given to the LLM to produce the final revised answer. This method has shown improvements in various question-answering and text-generation tasks.</p>
</div>
</section>
<section id="Ch2.S2.SS6.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Cumulative Reasoning</h6>

<div id="Ch2.S2.SS6.SSS0.Px6.p1" class="ltx_para">
<p id="Ch2.S2.SS6.SSS0.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib339" title="" class="ltx_ref">2023b</a>)</cite>  first generates several potential steps in answering the question. It then has a LLM evaluate them, deciding to either accept or reject these steps. Finally, it checks whether it has arrived at the final answer. If so, it terminates the process, but otherwise it repeats it. This method has demonstrated improvements in logical inference tasks and mathematical problem.</p>
</div>
</section>
</section>
</section>
<section id="Ch2.S3" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">2.3 </span>Prompting Technique Usage</h3>

<figure id="Ch2.F11" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_2">
<figure id="Ch2.F11.2" class="ltx_figure ltx_figure_panel ltx_minipage ltx_align_center ltx_align_top" style="width:208.1pt;">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><img src="/html/2406.06608/assets/x5.png" id="Ch2.F11.1.g1" class="ltx_graphics ltx_figure_panel ltx_img_portrait" width="384" height="645" alt="Refer to caption"></div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.9: </span>Citation Counts of GenAI Models</figcaption><div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><img src="/html/2406.06608/assets/x6.png" id="Ch2.F11.2.g2" class="ltx_graphics ltx_centering ltx_figure_panel ltx_img_portrait" width="384" height="645" alt="Refer to caption"></div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.10: </span>Citation Counts of Datasets</figcaption>
</figure>
</div>
<div class="ltx_flex_cell ltx_flex_size_2">
<figure id="Ch2.F11.3" class="ltx_figure ltx_figure_panel ltx_minipage ltx_align_center ltx_align_top" style="width:208.1pt;"><img src="/html/2406.06608/assets/x7.png" id="Ch2.F11.3.g1" class="ltx_graphics ltx_img_portrait" width="474" height="1199" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.11: </span>Citation Counts of Prompting Techniques. The top 25 papers in our dataset, measured by how often they are cited by other papers in our dataset. Most papers here are prompting techniques*, and the remaining papers contains prompting advice.</figcaption>
</figure>
</div>
</div>
</figure>
<div id="Ch2.S3.p1" class="ltx_para">
<p id="Ch2.S3.p1.1" class="ltx_p">As we have just seen, there exist many text-based prompting techniques. However, only a small subset of them are commonly used in research and in industry. We measure technique usage by proxy of measuring the number of citations by other papers in our dataset. We do so with the presumption that papers about prompting are more likely to actually use or evaluate the cited technique. We graph the top 25 papers cited in this way from our dataset and find that most of them propose new prompting techniques (Figure <a href="#Ch2.F11" title="Figure 2.11 ‣ 2.3 Prompting Technique Usage ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.11</span></a>). The prevalence of citations for Few-Shot and Chain-of-Thought prompting is unsurprising and helps to establish a baseline for understanding the prevalence of other techniques.</p>
</div>
<section id="Ch2.S3.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.3.1 </span>Benchmarks</h4>

<div id="Ch2.S3.SS1.p1" class="ltx_para">
<p id="Ch2.S3.SS1.p1.1" class="ltx_p">In prompting research, when researchers propose a new technique, they usually benchmark it across multiple models and datasets. This is important to prove the utility of the technique and examine how it transfers across models.</p>
</div>
<div id="Ch2.S3.SS1.p2" class="ltx_para">
<p id="Ch2.S3.SS1.p2.1" class="ltx_p">In order to make it easier for researchers proposing new techniques to know how to benchmark them, we quantitatively examine which models (Figure <a href="#Ch2.F11" title="Figure 2.11 ‣ 2.3 Prompting Technique Usage ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.11</span></a>) and what benchmark datasets (Figure <a href="#Ch2.F11" title="Figure 2.11 ‣ 2.3 Prompting Technique Usage ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.11</span></a>) are being used. Again, we measure usage by how many times papers in our dataset cite the benchmark datasets and models.</p>
</div>
<div id="Ch2.S3.SS1.p3" class="ltx_para">
<p id="Ch2.S3.SS1.p3.1" class="ltx_p">To find which datasets and models are being used, we prompted GPT-4-1106-preview to extract any mentioned dataset or model from the body of papers in our dataset. After, we manually filtered out results that were not models or datasets. The citation counts were acquired by searching items from the finalized list on Semantic Scholar.</p>
</div>
</section>
</section>
<section id="Ch2.S4" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">2.4 </span>Prompt Engineering</h3>

<div id="Ch2.S4.p1" class="ltx_para">
<p id="Ch2.S4.p1.1" class="ltx_p">In addition to surveying prompting technique, we also review prompt engineering techniques, which are used to automatically optimize prompts. We discuss some techniques that use gradient updates, since the set of prompt engineering techniques is much smaller than that of prompting techniques.</p>
</div>
<section id="Ch2.S4.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Meta Prompting</h6>

<div id="Ch2.S4.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px1.p1.1" class="ltx_p">is the process of prompting a LLM to generate or improve a prompt or prompt template <cite class="ltx_cite ltx_citemacro_cite">Reynolds and McDonell (<a href="#bib.bib233" title="" class="ltx_ref">2021</a>); Zhou et al. (<a href="#bib.bib354" title="" class="ltx_ref">2022b</a>); Ye et al. (<a href="#bib.bib329" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">AutoPrompt</h6>

<div id="Ch2.S4.SS0.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Shin et al. (<a href="#bib.bib260" title="" class="ltx_ref">2020b</a>)</cite> uses a frozen LLM as well as a prompt template that includes some "trigger tokens", whose values are updated via backpropogation at training time. This is a version of soft-prompting.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Automatic Prompt Engineer (APE)</h6>

<div id="Ch2.S4.SS0.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib354" title="" class="ltx_ref">2022b</a>)</cite> uses a set of exemplars to generate a Zero-Shot instruction prompt. It generates multiple possible prompts, scores them, then creates variations of the best ones (e.g. by using prompt paraphrasing). It iterates on this process until some desiderata are reached.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Gradientfree Instructional Prompt Search (GrIPS)</h6>

<div id="Ch2.S4.SS0.SSS0.Px4.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Prasad et al. (<a href="#bib.bib218" title="" class="ltx_ref">2023</a>)</cite> is similar to APE, but uses a more complex set of operations including deletion, addition, swapping, and paraphrasing in order to create variations of a starting prompt.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Optimization with Textual Gradients (ProTeGi)</h6>

<div id="Ch2.S4.SS0.SSS0.Px5.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px5.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Pryzant et al. (<a href="#bib.bib221" title="" class="ltx_ref">2023</a>)</cite> is a unique approach to prompt engineering that improves a prompt template through a multi-step process. First, it passes a batch of inputs through the template, then passes the output, ground truth, and prompt into another prompt that criticizes the original prompt. It generates new prompts from these criticisms then uses a bandit algorithm <cite class="ltx_cite ltx_citemacro_cite">Gabillon et al. (<a href="#bib.bib61" title="" class="ltx_ref">2011</a>)</cite> to select one. ProTeGi demonstrates improvements over methods like APE and GRIPS.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">RLPrompt</h6>

<div id="Ch2.S4.SS0.SSS0.Px6.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px6.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Deng et al. (<a href="#bib.bib45" title="" class="ltx_ref">2022</a>)</cite> uses a frozen LLM with an unfrozen module added. It uses this LLM to generate prompt templates, scores the templates on a dataset, and updates the unfrozen module using Soft Q-Learning <cite class="ltx_cite ltx_citemacro_cite">Guo et al. (<a href="#bib.bib80" title="" class="ltx_ref">2022</a>)</cite>. Interestingly, the method often selects grammatically gibberish text as the optimal prompt template.</p>
</div>
</section>
<section id="Ch2.S4.SS0.SSS0.Px7" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization (DP2O)</h6>

<div id="Ch2.S4.SS0.SSS0.Px7.p1" class="ltx_para">
<p id="Ch2.S4.SS0.SSS0.Px7.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib139" title="" class="ltx_ref">2023b</a>)</cite> is perhaps the most complicated prompt engineering technique, involving reinforcement learning, a custom prompt scoring function, and conversations the a LLM in order to construct the prompt.</p>
</div>
</section>
</section>
<section id="Ch2.S5" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">2.5 </span>Answer Engineering</h3>

<div id="Ch2.S5.p1" class="ltx_para">
<p id="Ch2.S5.p1.1" class="ltx_p">Answer engineering is the iterative process of developing or selecting among algorithms that extract precise answers from LLM outputs. To understand the need for answer engineering, consider a binary classification task where the labels are "Hate Speech" and "Not Hate Speech". The prompt template might look like this:</p>
</div>
<div id="Ch2.S5.p2" class="ltx_para ltx_noindent">
<svg id="Ch2.S5.p2.pic1" class="ltx_picture" height="41.4" overflow="visible" version="1.1" width="600"><g transform="translate(0,41.4) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 41.4 L 600 41.4 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 39.43 L 598.03 39.43 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="13.84" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch2.S5.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch2.S5.p2.pic1.1.1.1.1.1.1" class="ltx_p">Is this "Hate Speech" or "Not Hate Speech": {TEXT}</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch2.S5.p3" class="ltx_para">
<p id="Ch2.S5.p3.1" class="ltx_p">When a hate speech sample is put through the template, it might have outputs such as "It’s hate speech", "Hate Speech.", or even "Hate speech, because it uses negative language against a racial group". This variance in response formats is difficult to parse consistently; improved prompting can help, but only to a certain extent.</p>
</div>
<div id="Ch2.S5.p4" class="ltx_para">
<p id="Ch2.S5.p4.1" class="ltx_p">There are three design decisions in answer engineering, the choice of answer space, answer shape, and answer extractor (Figure <a href="#Ch2.F12" title="Figure 2.12 ‣ 2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.12</span></a>). <cite class="ltx_cite ltx_citemacro_citet">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite> define the first two as necessary components of answer engineering and we append the third. We consider answer engineering to be distinct from prompt engineering, but extremely closely related; the processes are often conducted in tandem.</p>
</div>
<figure id="Ch2.F12" class="ltx_figure"><img src="/html/2406.06608/assets/x8.png" id="Ch2.F12.g1" class="ltx_graphics ltx_centering ltx_img_landscape" width="461" height="229" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 2.12: </span>An annotated output of a LLM output for a labeling task, which shows the three design decisions of answer engineering: the choice of answer shape, space, and extractor. Since this is an output from a classification task, the answer shape could be restricted to a single token and the answer space to one of two tokens ("positive" or "negative"), though they are unrestricted in this image.</figcaption>
</figure>
<section id="Ch2.S5.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.5.1 </span>Answer Shape</h4>

<div id="Ch2.S5.SS1.p1" class="ltx_para">
<p id="Ch2.S5.SS1.p1.1" class="ltx_p">The shape of an answer is its physical format. For example, it could be a token, span of tokens, or even an image or video.<span id="Ch2.footnote6" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">6</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">6</sup><span class="ltx_tag ltx_tag_note">6</span>
We use a different definition than <cite class="ltx_cite ltx_citemacro_citet">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite> with respect to granularity (e.g. token vs span), since the output could be of a different modality.</span></span></span> It is sometimes useful to restrict the output shape of a LLM to a single token for tasks like binary classification.</p>
</div>
</section>
<section id="Ch2.S5.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.5.2 </span>Answer Space</h4>

<div id="Ch2.S5.SS2.p1" class="ltx_para">
<p id="Ch2.S5.SS2.p1.1" class="ltx_p">The space of an answer is the domain of values that its structure may contain. This may simply be the space of all tokens, or in a binary labeling task, could just be two possible tokens.</p>
</div>
</section>
<section id="Ch2.S5.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">2.5.3 </span>Answer Extractor</h4>

<div id="Ch2.S5.SS3.p1" class="ltx_para">
<p id="Ch2.S5.SS3.p1.1" class="ltx_p">In cases where it is impossible to entirely control the answer space (e.g. consumer-facing LLMs), or the expected answer may be located somewhere within the model output, a rule can be defined to extract the final answer. This rule is often a simple function (e.g. a regular expression), but can also use a separate LLM to extract the answer.</p>
</div>
<section id="Ch2.S5.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Verbalizer</h6>

<div id="Ch2.S5.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch2.S5.SS3.SSS0.Px1.p1.1" class="ltx_p">Often used in labeling tasks, a verbalizer maps a token, span, or other type of output to a label and vice-versa (injective) <cite class="ltx_cite ltx_citemacro_cite">Schick and Schütze (<a href="#bib.bib244" title="" class="ltx_ref">2021</a>)</cite>. For example, if we wish for a model to predict whether a Tweet is positive or negative, we could prompt it to output either "+" or "-" and a verbalizer would map these token sequences to the appropriate labels. The selection of a verbalizer constitutes a component of answer engineering.</p>
</div>
</section>
<section id="Ch2.S5.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Regex</h6>

<div id="Ch2.S5.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="Ch2.S5.SS3.SSS0.Px2.p1.1" class="ltx_p">As mentioned previously, Regexes are often used to extract answers. They are usually used to search for the first instance of a label. However, depending on the output format and whether CoTs are generated, it may be better to search for the last instance.</p>
</div>
</section>
<section id="Ch2.S5.SS3.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Separate LLM</h6>

<div id="Ch2.S5.SS3.SSS0.Px3.p1" class="ltx_para">
<p id="Ch2.S5.SS3.SSS0.Px3.p1.1" class="ltx_p">Sometimes outputs are so complicated that regexes won’t work consistently. In this case, it can be useful to have a separate LLM evaluate the output and extract an answer.</p>
</div>
</section>
</section>
</section>
</section>
<section id="Ch3" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">3   Beyond English Text Prompting</h2>

<div id="Ch3.p1" class="ltx_para ltx_noindent">
<p id="Ch3.p1.1" class="ltx_p">Prompting GenAIs with English text currently stands as the dominant method for interaction. Prompting in other languages or through different modalities often requires special techniques to achieve comparable performance. In this context, we discuss the domains of multilingual and multimodal prompting.</p>
</div>
<figure id="Ch3.F1" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch3.F1.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch3.F1.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
font=, rectangle,
draw=black, rounded corners,
align=left,
minimum width=2em, edge+=darkgray, line width=1pt,
s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
text width=7em, ,
[Multilingual Techniques, fill=teal!50
[Chain-of-Thought <a href="#Ch3.S1.SS1" title="3.1.1 Chain-of-Thought (CoT) ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.1</span></a>, fill=red!50
[XLT <a href="#Ch3.S1.SS1.SSS0.Px1" title="XLT (Cross-Lingual Thought) Prompting ‣ 3.1.1 Chain-of-Thought (CoT) ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.1</span></a>, fill=red!40]
[CLSP <a href="#Ch3.S1.SS1.SSS0.Px2" title="Cross-Lingual Self Consistent Prompting (CLSP) ‣ 3.1.1 Chain-of-Thought (CoT) ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.1</span></a>, fill=red!40]]
[In-Context Learning <a href="#Ch3.S1.SS2" title="3.1.2 In-Context Learning ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.2</span></a>, fill=green!50
[X-InSTA <a href="#Ch3.S1.SS2.SSS0.Px1" title="X-InSTA Prompting ‣ 3.1.2 In-Context Learning ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.2</span></a>, fill=green!40]
[In-CLT <a href="#Ch3.S1.SS2.SSS0.Px2" title="In-CLT (Cross-lingual Transfer) Prompting ‣ 3.1.2 In-Context Learning ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.2</span></a>, fill=green!40]
]
[In-Context Ex. Selection <a href="#Ch3.S1.SS3" title="3.1.3 In-Context Example Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.3</span></a>, fill=green!50
[PARC <a href="#Ch3.S1.SS3.SSS0.Px1" title="PARC (Prompts Augmented by Retrieval Cross-lingually) ‣ 3.1.3 In-Context Example Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.3</span></a>, fill=green!40]
[Semantically-Aligned <a href="#Ch3.S1.SS3" title="3.1.3 In-Context Example Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.3</span></a>, fill=green!40]
[Semantically-Distant <a href="#Ch3.S1.SS3" title="3.1.3 In-Context Example Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.3</span></a>, fill=green!40]
]
[Human-in-the-Loop <a href="#Ch3.S1.SS5.SSS1" title="3.1.5.1 Human-in-the-Loop ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5.1</span></a>, fill=blue!50
[Interactive Chain <a href="#Ch3.S1.SS5.SSS1.Px1" title="Interactive-Chain-Prompting (ICP) ‣ 3.1.5.1 Human-in-the-Loop ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5.1</span></a>, fill=blue!40]
[Iterative <a href="#Ch3.S1.SS5.SSS1.Px2" title="Iterative Prompting ‣ 3.1.5.1 Human-in-the-Loop ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5.1</span></a>, fill=blue!40]
]
[Translation <a href="#Ch3.S1.SS5" title="3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5</span></a>, fill=orange!50
[Chain-of-Dictionary <a href="#Ch3.S1.SS5.SSS0.Px2" title="Chain-of-Dictionary (CoD) ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5</span></a>, fill=orange!40]
[DecoMT <a href="#Ch3.S1.SS5.SSS0.Px4" title="Decomposed Prompting for MT (DecoMT) ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5</span></a>, fill=orange!40]
[DiPMT <a href="#Ch3.S1.SS5.SSS0.Px3" title="Dictionary-based Prompting for Machine Translation (DiPMT) ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5</span></a>, fill=orange!40]
[MAPS <a href="#Ch3.S1.SS5.SSS0.Px1" title="Multi-Aspect Prompting and Selection (MAPS) ‣ 3.1.5 Prompting for Machine Translation ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.5</span></a>, fill=orange!40]
]
[Translate First Prompting <a href="#Ch3.S1.SS0.SSS0.Px1" title="Translate First Prompting ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1</span></a>, fill=violet!50
[External MT Systems <a href="#Ch3.S1.SS0.SSS0.Px1" title="Translate First Prompting ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1</span></a>, fill=violet!40]
[Standard LLMs <a href="#Ch3.S1.SS0.SSS0.Px1" title="Translate First Prompting ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1</span></a>, fill=violet!40]
[Multilingual LLMs <a href="#Ch3.S1.SS0.SSS0.Px1" title="Translate First Prompting ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1</span></a>, fill=violet!40]
]
[Prompt Language <a href="#Ch3.S1.SS4" title="3.1.4 Prompt Template Language Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.4</span></a>, fill=brown!50
[English <a href="#Ch3.S1.SS4.SSS0.Px1" title="English Prompt Template ‣ 3.1.4 Prompt Template Language Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.4</span></a>, fill=brown!40]
[Task Language <a href="#Ch3.S1.SS4.SSS0.Px2" title="Task Language Prompt Template ‣ 3.1.4 Prompt Template Language Selection ‣ 3.1 Multilingual ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.1.4</span></a>, fill=brown!40]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 3.1: </span>All multilingual prompting techniques.</figcaption>
</figure>
<section id="Ch3.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">3.1 </span>Multilingual</h3>

<div id="Ch3.S1.p1" class="ltx_para">
<p id="Ch3.S1.p1.1" class="ltx_p">State-of-the-art GenAIs have often been predominately trained with English dataset, leading to a notable disparity in the output quality in languages other than English, particularly low-resource languages <cite class="ltx_cite ltx_citemacro_cite">Bang et al. (<a href="#bib.bib14" title="" class="ltx_ref">2023</a>); Jiao et al. (<a href="#bib.bib107" title="" class="ltx_ref">2023</a>); Hendy et al. (<a href="#bib.bib90" title="" class="ltx_ref">2023</a>); Shi et al. (<a href="#bib.bib258" title="" class="ltx_ref">2022</a>)</cite>.
As a result, various multilingual prompting techniques have emerged in an attempt to improve model performance in non-English settings.</p>
</div>
<section id="Ch3.S1.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Translate First Prompting</h6>

<div id="Ch3.S1.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS0.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Shi et al. (<a href="#bib.bib258" title="" class="ltx_ref">2022</a>)</cite> 
is perhaps the simplest strategy and first translates non-English input examples into English. By translating the inputs into English, the model can utilize its strengths in English to better understand the content. Translation tools vary; <cite class="ltx_cite ltx_citemacro_citet">Shi et al. (<a href="#bib.bib258" title="" class="ltx_ref">2022</a>)</cite> use an external MT system, <cite class="ltx_cite ltx_citemacro_citet">Etxaniz et al. (<a href="#bib.bib53" title="" class="ltx_ref">2023</a>)</cite> prompt multilingual LMs and <cite class="ltx_cite ltx_citemacro_citet">Awasthi et al. (<a href="#bib.bib9" title="" class="ltx_ref">2023</a>)</cite> prompt LLMs to translate non-English inputs.</p>
</div>
</section>
<section id="Ch3.S1.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.1.1 </span>Chain-of-Thought (CoT)</h4>

<div id="Ch3.S1.SS1.p1" class="ltx_para">
<p id="Ch3.S1.SS1.p1.1" class="ltx_p">CoT prompting <cite class="ltx_cite ltx_citemacro_cite">Wei et al. (<a href="#bib.bib300" title="" class="ltx_ref">2023a</a>)</cite> has been extended to the multilingual setting in multiple ways.</p>
</div>
<section id="Ch3.S1.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">XLT (Cross-Lingual Thought) Prompting</h6>

<div id="Ch3.S1.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS1.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Huang et al. (<a href="#bib.bib97" title="" class="ltx_ref">2023a</a>)</cite>  utilizes a prompt template composed of six separate instructions, including role assignment, cross-lingual thinking, and CoT.</p>
</div>
</section>
<section id="Ch3.S1.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Cross-Lingual Self Consistent Prompting (CLSP)</h6>

<div id="Ch3.S1.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch3.S1.SS1.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Qin et al. (<a href="#bib.bib225" title="" class="ltx_ref">2023a</a>)</cite>  introduces an ensemble technique that constructs reasoning paths in different languages to answer the same question.</p>
</div>
</section>
</section>
<section id="Ch3.S1.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.1.2 </span>In-Context Learning</h4>

<div id="Ch3.S1.SS2.p1" class="ltx_para">
<p id="Ch3.S1.SS2.p1.1" class="ltx_p">ICL has also been extended to multilingual settings in multiple ways.</p>
</div>
<section id="Ch3.S1.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">X-InSTA Prompting</h6>

<div id="Ch3.S1.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS2.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Tanwar et al. (<a href="#bib.bib275" title="" class="ltx_ref">2023</a>)</cite>  explores three distinct approaches for aligning in-context examples with the input sentence for classification tasks: using semantically similar examples to the input (semantic alignment), examples that share the same label as the input (task-based alignment), and the combination of both semantic and task-based alignments.</p>
</div>
</section>
<section id="Ch3.S1.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">In-CLT (Cross-lingual Transfer) Prompting</h6>

<div id="Ch3.S1.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch3.S1.SS2.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_citep">(Kim et al., <a href="#bib.bib122" title="" class="ltx_ref">2023</a>)</cite>  leverages both the source and target languages to create in-context examples, diverging from the traditional method of using source language exemplars. This strategy helps stimulate the cross-lingual cognitive capabilities of multilingual LLMs, thus boosting performance on cross-lingual tasks.</p>
</div>
</section>
</section>
<section id="Ch3.S1.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.1.3 </span>In-Context Example Selection</h4>

<div id="Ch3.S1.SS3.p1" class="ltx_para">
<p id="Ch3.S1.SS3.p1.1" class="ltx_p">In-context example selection heavily influences the multilingual performance of LLMs <cite class="ltx_cite ltx_citemacro_cite">Garcia et al. (<a href="#bib.bib69" title="" class="ltx_ref">2023</a>); Agrawal et al. (<a href="#bib.bib2" title="" class="ltx_ref">2023</a>)</cite>. Finding in-context examples that are semantically similar to the source text is very important <cite class="ltx_cite ltx_citemacro_cite">Winata et al. (<a href="#bib.bib309" title="" class="ltx_ref">2023</a>); Moslem et al. (<a href="#bib.bib187" title="" class="ltx_ref">2023</a>); Sia and Duh (<a href="#bib.bib267" title="" class="ltx_ref">2023</a>)</cite>. However, using semantically dissimilar (<span id="Ch3.S1.SS3.p1.1.1" class="ltx_text ltx_font_italic">peculiar</span>) exemplars has also been shown to enhance performance <cite class="ltx_cite ltx_citemacro_cite">Kim and Komachi (<a href="#bib.bib120" title="" class="ltx_ref">2023</a>)</cite>. This same contrast exists in the English-only setting. Additionally, when dealing with ambiguous sentences, selecting exemplars with polysemous or rare word senses may boost performance <cite class="ltx_cite ltx_citemacro_cite">Iyer et al. (<a href="#bib.bib102" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
<section id="Ch3.S1.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">PARC (Prompts Augmented by Retrieval Cross-lingually)</h6>

<div id="Ch3.S1.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS3.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Nie et al. (<a href="#bib.bib194" title="" class="ltx_ref">2023</a>)</cite> introduce a framework that retrieves relevant exemplars from a high resource language. This framework is specifically designed to enhance cross-lingual transfer performance, particularly for low-resource target languages. <cite class="ltx_cite ltx_citemacro_citet">Li et al. (<a href="#bib.bib147" title="" class="ltx_ref">2023g</a>)</cite> extend this work to Bangla.</p>
</div>
</section>
</section>
<section id="Ch3.S1.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.1.4 </span>Prompt Template Language Selection</h4>

<div id="Ch3.S1.SS4.p1" class="ltx_para">
<p id="Ch3.S1.SS4.p1.1" class="ltx_p">In multilingual prompting, the selection of language for the prompt template can markedly influence the model performance.</p>
</div>
<section id="Ch3.S1.SS4.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">English Prompt Template</h6>

<div id="Ch3.S1.SS4.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS4.SSS0.Px1.p1.1" class="ltx_p">Constructing the prompt template in English is often more effective than in the task language for multilingual tasks. This is likely due to the predominance of English data during LLM pre-training <cite class="ltx_cite ltx_citemacro_cite">Lin et al. (<a href="#bib.bib154" title="" class="ltx_ref">2022</a>); Ahuja et al. (<a href="#bib.bib3" title="" class="ltx_ref">2023</a>)</cite>. <cite class="ltx_cite ltx_citemacro_citet">Lin et al. (<a href="#bib.bib154" title="" class="ltx_ref">2022</a>)</cite> suggest that this is likely due to a high overlap with pre-training data and vocabulary. Similarly, <cite class="ltx_cite ltx_citemacro_citet">Ahuja et al. (<a href="#bib.bib3" title="" class="ltx_ref">2023</a>)</cite> highlight how translation errors when creating task language templates propagate in the form of incorrect syntax and semantics, adversely affecting task performance. Further, <cite class="ltx_cite ltx_citemacro_citet">Fu et al. (<a href="#bib.bib59" title="" class="ltx_ref">2022</a>)</cite> compare in-lingual (task language) prompts and cross-lingual (mixed language) prompts and find the cross-lingual approach to be more effective, likely because it uses more English in the prompt, thus facilitating retrieving knowledge from the model.</p>
</div>
</section>
<section id="Ch3.S1.SS4.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Task Language Prompt Template</h6>

<div id="Ch3.S1.SS4.SSS0.Px2.p1" class="ltx_para">
<p id="Ch3.S1.SS4.SSS0.Px2.p1.1" class="ltx_p">In contrast, many multilingual prompting benchmarks such as BUFFET <cite class="ltx_cite ltx_citemacro_cite">Asai et al. (<a href="#bib.bib7" title="" class="ltx_ref">2023</a>)</cite> or LongBench <cite class="ltx_cite ltx_citemacro_cite">Bai et al. (<a href="#bib.bib10" title="" class="ltx_ref">2023a</a>)</cite> use task language prompts for language-specific use cases. <cite class="ltx_cite ltx_citemacro_citet">Muennighoff et al. (<a href="#bib.bib189" title="" class="ltx_ref">2023</a>)</cite> specifically studies different translation methods when constructing native-language prompts. They demonstrate that human translated prompts are superior to their machine-translated counterparts. Native or non-native template performance can differ across tasks and models <cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib149" title="" class="ltx_ref">2023h</a>)</cite>. As such, neither option will always be the best approach <cite class="ltx_cite ltx_citemacro_cite">Nambi et al. (<a href="#bib.bib190" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
</section>
<section id="Ch3.S1.SS5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.1.5 </span>Prompting for Machine Translation</h4>

<div id="Ch3.S1.SS5.p1" class="ltx_para">
<p id="Ch3.S1.SS5.p1.1" class="ltx_p">There is significant research into leveraging GenAI to facilitate accurate and nuanced translation. Although this is a specific application of prompting, many of these techniques are important more broadly for multilingual prompting.</p>
</div>
<section id="Ch3.S1.SS5.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Multi-Aspect Prompting and Selection (MAPS)</h6>

<div id="Ch3.S1.SS5.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">He et al. (<a href="#bib.bib88" title="" class="ltx_ref">2023b</a>)</cite> mimics the human translation process, which involves multiple preparatory steps to ensure high-quality output. This framework starts with knowledge mining from the source sentence (extracting keywords and topics, and generating translation exemplars). It integrates this knowledge to generate multiple possible translations, then selects the best one.</p>
</div>
</section>
<section id="Ch3.S1.SS5.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Chain-of-Dictionary (CoD)</h6>

<div id="Ch3.S1.SS5.SSS0.Px2.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib169" title="" class="ltx_ref">2023b</a>)</cite>  first extracts words from the source phrase, then makes a list of their meanings in multiple languages, automatically via retrieval from a dictionary (e.g. English: ‘<span id="Ch3.S1.SS5.SSS0.Px2.p1.1.1" class="ltx_text ltx_font_italic">apple</span>’, Spanish: ‘<span id="Ch3.S1.SS5.SSS0.Px2.p1.1.2" class="ltx_text ltx_font_italic">manzana</span>’). Then, they prepend these dictionary phrases to the prompt, where it asks a GenAI to use them during translation.</p>
</div>
</section>
<section id="Ch3.S1.SS5.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Dictionary-based Prompting for Machine Translation (DiPMT)</h6>

<div id="Ch3.S1.SS5.SSS0.Px3.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Ghazvininejad et al. (<a href="#bib.bib72" title="" class="ltx_ref">2023</a>)</cite> works similarly to CoD, but only gives definitions in the source and target languages, and formats them slightly differently.</p>
</div>
</section>
<section id="Ch3.S1.SS5.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Decomposed Prompting for MT (DecoMT)</h6>

<div id="Ch3.S1.SS5.SSS0.Px4.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Puduppully et al. (<a href="#bib.bib222" title="" class="ltx_ref">2023</a>)</cite> divides the source text into several chunks and translates them independently using few-shot prompting. Then it uses these translations and contextual information between chunks to generate a final translation.</p>
</div>
</section>
<section id="Ch3.S1.SS5.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">3.1.5.1 </span>Human-in-the-Loop</h5>

<section id="Ch3.S1.SS5.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Interactive-Chain-Prompting (ICP)</h6>

<div id="Ch3.S1.SS5.SSS1.Px1.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS1.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Pilault et al. (<a href="#bib.bib215" title="" class="ltx_ref">2023</a>)</cite> deals with potential ambiguities in translation by first asking the GenAI to generate sub-questions about any ambiguities in the phrase to be translated. Humans later respond to these questions and the system includes this information to generate a final translation.</p>
</div>
</section>
<section id="Ch3.S1.SS5.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Iterative Prompting</h6>

<div id="Ch3.S1.SS5.SSS1.Px2.p1" class="ltx_para">
<p id="Ch3.S1.SS5.SSS1.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yang et al. (<a href="#bib.bib322" title="" class="ltx_ref">2023d</a>)</cite> also involves humans during translation. First, they prompt LLMs to create a draft translation. This initial version is further refined by integrating supervision signals obtained from either automated retrieval systems or direct human feedback.</p>
</div>
<figure id="Ch3.F2" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch3.F2.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch3.F2.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
font=, rectangle,
draw=black, rounded corners,
align=left,
minimum width=2em, edge+=darkgray, line width=1pt,
s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
text width=7em, ,
[Multimodal (MM) Techniques, fill=teal!50
[Image <a href="#Ch3.S2.SS1" title="3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1</span></a>, fill=red!50
[MM. CoT <a href="#Ch3.S2.SS1.SSS2" title="3.2.1.2 Multimodal Chain-of-Thought ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.2</span></a>, fill=red!40
[Chain-of-Images <a href="#Ch3.S2.SS1.SSS2.Px3" title="Chain-of-Images (CoI) ‣ 3.2.1.2 Multimodal Chain-of-Thought ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.2</span></a>, fill=red!30]
[Duty Distinct CoT <a href="#Ch3.S2.SS1.SSS2.Px1" title="Duty Distinct Chain-of-Thought (DDCoT) ‣ 3.2.1.2 Multimodal Chain-of-Thought ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.2</span></a>, fill=red!30]
[MM Graph-of-Thought <a href="#Ch3.S2.SS1.SSS2.Px2" title="Multimodal Graph-of-Thought ‣ 3.2.1.2 Multimodal Chain-of-Thought ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.2</span></a>, fill=red!30]
]
[Multimodal ICL <a href="#Ch3.S2.SS1.SSS1" title="3.2.1.1 Multimodal In-Context Learning ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.1</span></a>, fill=red!40
[Image-as-Text Prompt<a href="#Ch3.S2.SS1.SSS1.Px2" title="Image-as-Text Prompting ‣ 3.2.1.1 Multimodal In-Context Learning ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.1</span></a>, fill=red!30]
[Paired-Image Prompt <a href="#Ch3.S2.SS1.SSS1.Px1" title="Paired-Image Prompting ‣ 3.2.1.1 Multimodal In-Context Learning ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1.1</span></a>, fill=red!30]
]
[Negative Prompt <a href="#Ch3.S2.SS1.SSS0.Px2" title="Negative Prompting ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1</span></a>, fill=red!40]
[Prompt Modifiers <a href="#Ch3.S2.SS1.SSS0.Px1" title="Prompt Modifiers ‣ 3.2.1 Image Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.1</span></a>, fill=red!40]
]
[Segmentation Prompting <a href="#Ch3.S2.SS4" title="3.2.4 Segmentation Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.4</span></a>, fill=blue!50]
[Video <a href="#Ch3.S2.SS3" title="3.2.3 Video Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.3</span></a>, fill=green!50
[Video Gen. <a href="#Ch3.S2.SS3.SSS1" title="3.2.3.1 Video Generation Techniques ‣ 3.2.3 Video Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.3.1</span></a>, fill=green!40
]
]
[3D Prompting <a href="#Ch3.S2.SS5" title="3.2.5 3D Prompting ‣ 3.2 Multimodal ‣ 3 Beyond English Text Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">3.2.5</span></a>, fill=orange!50]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 3.2: </span>All multimodal prompting techniques.</figcaption>
</figure>
</section>
</section>
</section>
</section>
<section id="Ch3.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">3.2 </span>Multimodal</h3>

<div id="Ch3.S2.p1" class="ltx_para">
<p id="Ch3.S2.p1.1" class="ltx_p">As GenAI models evolve beyond text-based domains, new prompting techniques emerge. These multimodal prompting technique are often not simply applications of text-based prompting techniques, but entirely novel ideas made possible by different modalities. We now extend our text-based taxonomy to include a mixture of multimodal analogs of text-based prompting techniques as well as completely novel multimodal techniques.</p>
</div>
<section id="Ch3.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.2.1 </span>Image Prompting</h4>

<div id="Ch3.S2.SS1.p1" class="ltx_para">
<p id="Ch3.S2.SS1.p1.1" class="ltx_p">The image modality encompasses data such as photographs, drawings, or even screenshots of text <cite class="ltx_cite ltx_citemacro_cite">Gong et al. (<a href="#bib.bib74" title="" class="ltx_ref">2023</a>)</cite>. Image prompting may refer to prompts that either contain images or are used to generate images. Common tasks include image generation <cite class="ltx_cite ltx_citemacro_cite">Ding et al. (<a href="#bib.bib49" title="" class="ltx_ref">2021</a>); Hinz et al. (<a href="#bib.bib93" title="" class="ltx_ref">2022</a>); Tao et al. (<a href="#bib.bib276" title="" class="ltx_ref">2022</a>); Li et al. (<a href="#bib.bib137" title="" class="ltx_ref">2019a</a>, <a href="#bib.bib143" title="" class="ltx_ref">b</a>); Rombach et al. (<a href="#bib.bib235" title="" class="ltx_ref">2022</a>)</cite>, caption generation <cite class="ltx_cite ltx_citemacro_cite">Li et al. (<a href="#bib.bib148" title="" class="ltx_ref">2020</a>)</cite>, image classification <cite class="ltx_cite ltx_citemacro_cite">Khalil et al. (<a href="#bib.bib115" title="" class="ltx_ref">2023</a>)</cite>, and image editing <cite class="ltx_cite ltx_citemacro_cite">Crowson et al. (<a href="#bib.bib41" title="" class="ltx_ref">2022</a>); Kwon and Ye (<a href="#bib.bib129" title="" class="ltx_ref">2022</a>); Bar-Tal et al. (<a href="#bib.bib16" title="" class="ltx_ref">2022</a>); Hertz et al. (<a href="#bib.bib91" title="" class="ltx_ref">2022</a>)</cite>. We now describe various image prompting techniques used for such applications.</p>
</div>
<section id="Ch3.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Modifiers</h6>

<div id="Ch3.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS0.Px1.p1.1" class="ltx_p">are simply words appended to a prompt to change the resultant image <cite class="ltx_cite ltx_citemacro_cite">Oppenlaender (<a href="#bib.bib197" title="" class="ltx_ref">2023</a>)</cite>. Components such as Medium (e.g. "on canvas") or Lighting (e.g. "a well lit scene") are often used.</p>
</div>
</section>
<section id="Ch3.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Negative Prompting</h6>

<div id="Ch3.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS0.Px2.p1.1" class="ltx_p">allows users to numerically weight certain terms in the prompt so that the model considers them more/less heavily than others. For example, by negatively weighting the terms “bad hands” and “extra digits”, models may be more likely to generate anatomically accurate hands <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>.</p>
</div>
</section>
<section id="Ch3.S2.SS1.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">3.2.1.1 </span>Multimodal In-Context Learning</h5>

<div id="Ch3.S2.SS1.SSS1.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS1.p1.1" class="ltx_p">The success of ICL in text-based settings has prompted research into multimodal ICL <cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib297" title="" class="ltx_ref">2023k</a>); Dong et al. (<a href="#bib.bib50" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
<section id="Ch3.S2.SS1.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Paired-Image Prompting</h6>

<div id="Ch3.S2.SS1.SSS1.Px1.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS1.Px1.p1.1" class="ltx_p">shows the model two images: one before and one after some transformation. Then, present the model with a new image for which it will perform the demonstrated conversion. This can be done either with textual instructions <cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib297" title="" class="ltx_ref">2023k</a>)</cite> or without them <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib162" title="" class="ltx_ref">2023e</a>)</cite>.</p>
</div>
</section>
<section id="Ch3.S2.SS1.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Image-as-Text Prompting</h6>

<div id="Ch3.S2.SS1.SSS1.Px2.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS1.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Hakimov and Schlangen (<a href="#bib.bib85" title="" class="ltx_ref">2023</a>)</cite> generates a textual description of an image. This allows for the easy inclusion of the image (or multiple images) in a text-based prompt.</p>
</div>
</section>
</section>
<section id="Ch3.S2.SS1.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">3.2.1.2 </span>Multimodal Chain-of-Thought</h5>

<div id="Ch3.S2.SS1.SSS2.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS2.p1.1" class="ltx_p">CoT has been extended to the image domain in various ways <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib343" title="" class="ltx_ref">2023d</a>); Huang et al. (<a href="#bib.bib100" title="" class="ltx_ref">2023c</a>); Zheng et al. (<a href="#bib.bib350" title="" class="ltx_ref">2023b</a>); Yao et al. (<a href="#bib.bib327" title="" class="ltx_ref">2023c</a>)</cite>. A simple example of this would be a prompt containing an image of a math problem accompanied by the textual instructions "Solve this step by step".</p>
</div>
<section id="Ch3.S2.SS1.SSS2.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Duty Distinct Chain-of-Thought (DDCoT)</h6>

<div id="Ch3.S2.SS1.SSS2.Px1.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS2.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zheng et al. (<a href="#bib.bib350" title="" class="ltx_ref">2023b</a>)</cite>  extends Least-to-Most prompting <cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib353" title="" class="ltx_ref">2022a</a>)</cite> to the multimodal setting, creating subquestions, then solving them and combining the answers into a final response.</p>
</div>
</section>
<section id="Ch3.S2.SS1.SSS2.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Multimodal Graph-of-Thought</h6>

<div id="Ch3.S2.SS1.SSS2.Px2.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS2.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yao et al. (<a href="#bib.bib327" title="" class="ltx_ref">2023c</a>)</cite> extends Graph-of-Thought <cite class="ltx_cite ltx_citemacro_citet">Zhang et al. (<a href="#bib.bib343" title="" class="ltx_ref">2023d</a>)</cite> to the multimodal setting. GoT-Input also uses a two step rationale then answer process. At inference time, the the input prompt is used to construct a thought graph, which is then used along with the original prompt to generate a rationale to answer the question. When an image is input along with the question, an image captioning model is employed to generate a textual description of the image, which is then appended to the prompt before the thought graph construction to provide visual context.</p>
</div>
</section>
<section id="Ch3.S2.SS1.SSS2.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Chain-of-Images (CoI)</h6>

<div id="Ch3.S2.SS1.SSS2.Px3.p1" class="ltx_para">
<p id="Ch3.S2.SS1.SSS2.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Meng et al. (<a href="#bib.bib180" title="" class="ltx_ref">2023</a>)</cite> is a multimodal extension of Chain-of-Thought prompting, that generates images as part of its thought process. They use the prompt “Let’s think image by image” to generate SVGs, which the model can then use to reason visually.</p>
</div>
</section>
</section>
</section>
<section id="Ch3.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.2.2 </span>Audio Prompting</h4>

<div id="Ch3.S2.SS2.p1" class="ltx_para">
<p id="Ch3.S2.SS2.p1.1" class="ltx_p">Prompting has also been extended to the audio modality. Experiments with audio ICL have generated mixed results, with some open source audio models failing to perform ICL <cite class="ltx_cite ltx_citemacro_cite">Hsu et al. (<a href="#bib.bib95" title="" class="ltx_ref">2023</a>)</cite>. However, other results do show an ICL ability in audio models <cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib291" title="" class="ltx_ref">2023g</a>); Peng et al. (<a href="#bib.bib205" title="" class="ltx_ref">2023</a>); Chang et al. (<a href="#bib.bib27" title="" class="ltx_ref">2023</a>)</cite>. Audio prompting is currently in early stages, but we expect to see various prompting techniques proposed in the future.</p>
</div>
</section>
<section id="Ch3.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.2.3 </span>Video Prompting</h4>

<div id="Ch3.S2.SS3.p1" class="ltx_para">
<p id="Ch3.S2.SS3.p1.1" class="ltx_p">Prompting has also been extended to the video modality, for use in text-to-video generation <cite class="ltx_cite ltx_citemacro_cite">Brooks et al. (<a href="#bib.bib21" title="" class="ltx_ref">2024</a>); Lv et al. (<a href="#bib.bib174" title="" class="ltx_ref">2023</a>); Liang et al. (<a href="#bib.bib152" title="" class="ltx_ref">2023</a>); Girdhar et al. (<a href="#bib.bib73" title="" class="ltx_ref">2023</a>)</cite>, video editing <cite class="ltx_cite ltx_citemacro_cite">Zuo et al. (<a href="#bib.bib357" title="" class="ltx_ref">2023</a>); Wu et al. (<a href="#bib.bib310" title="" class="ltx_ref">2023a</a>); Cheng et al. (<a href="#bib.bib36" title="" class="ltx_ref">2023</a>)</cite>, and video-to-text generation <cite class="ltx_cite ltx_citemacro_cite">Yousaf et al. (<a href="#bib.bib333" title="" class="ltx_ref">2023</a>); Mi et al. (<a href="#bib.bib182" title="" class="ltx_ref">2023</a>); Ko et al. (<a href="#bib.bib123" title="" class="ltx_ref">2023a</a>)</cite>.</p>
</div>
<section id="Ch3.S2.SS3.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">3.2.3.1 </span>Video Generation Techniques</h5>

<div id="Ch3.S2.SS3.SSS1.p1" class="ltx_para">
<p id="Ch3.S2.SS3.SSS1.p1.1" class="ltx_p">When prompting a model to generate video, various modalities of prompts can be used as input, and several prompt-related techniques are often employed to enhance video generation. Image related techniques, such as prompt modifiers can often be used for video generation <cite class="ltx_cite ltx_citemacro_cite">Runway (<a href="#bib.bib237" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
</section>
<section id="Ch3.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.2.4 </span>Segmentation Prompting</h4>

<div id="Ch3.S2.SS4.p1" class="ltx_para">
<p id="Ch3.S2.SS4.p1.1" class="ltx_p">Prompting can also be used for segmentation (e.g. semantic segmentation) <cite class="ltx_cite ltx_citemacro_cite">Tang et al. (<a href="#bib.bib274" title="" class="ltx_ref">2023</a>); Liu et al. (<a href="#bib.bib160" title="" class="ltx_ref">2023c</a>)</cite>.</p>
</div>
</section>
<section id="Ch3.S2.SS5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">3.2.5 </span>3D Prompting</h4>

<div id="Ch3.S2.SS5.p1" class="ltx_para">
<p id="Ch3.S2.SS5.p1.1" class="ltx_p">Prompting can also be used in 3D modalities, for example in 3D object synthesis <cite class="ltx_cite ltx_citemacro_cite">Feng et al. (<a href="#bib.bib56" title="" class="ltx_ref">2023</a>); Li et al. (<a href="#bib.bib141" title="" class="ltx_ref">2023d</a>, <a href="#bib.bib140" title="" class="ltx_ref">c</a>); Lin et al. (<a href="#bib.bib153" title="" class="ltx_ref">2023</a>); Chen et al. (<a href="#bib.bib34" title="" class="ltx_ref">2023f</a>); Lorraine et al. (<a href="#bib.bib167" title="" class="ltx_ref">2023</a>); Poole et al. (<a href="#bib.bib216" title="" class="ltx_ref">2022</a>); Jain et al. (<a href="#bib.bib103" title="" class="ltx_ref">2022</a>)</cite>, 3D surface texturing <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib164" title="" class="ltx_ref">2023g</a>); Yang et al. (<a href="#bib.bib320" title="" class="ltx_ref">2023b</a>); Le et al. (<a href="#bib.bib132" title="" class="ltx_ref">2023</a>); Pajouheshgar et al. (<a href="#bib.bib200" title="" class="ltx_ref">2023</a>)</cite>, and 4D scene generation (animating a 3D scene) <cite class="ltx_cite ltx_citemacro_cite">Singer et al. (<a href="#bib.bib269" title="" class="ltx_ref">2023</a>); Zhao et al. (<a href="#bib.bib347" title="" class="ltx_ref">2023c</a>)</cite>, where input prompt modalities include text, image, user annotation (bounding boxes, points, lines), and 3D objects.</p>
</div>
</section>
</section>
</section>
<section id="Ch4" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">4   Extensions of Prompting</h2>

<div id="Ch4.p1" class="ltx_para ltx_noindent">
<p id="Ch4.p1.1" class="ltx_p">The techniques we have discussed thus far can be extremely complicated, incorporating many steps and iterations. However, we can take prompting further by adding access to external tools (agents) and complex evaluation algorithms judge the validity of LLM outputs.</p>
</div>
<figure id="Ch4.F1" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch4.F1.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch4.F1.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
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[CRITIC <a href="#Ch4.S1.SS1.SSS0.Px2" title="Self-Correcting with Tool-Interactive Critiquing (CRITIC) ‣ 4.1.1 Tool Use Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.1</span></a>, fill=red!40]
[MRKL Sys. <a href="#Ch4.S1.SS1.SSS0.Px1" title="Modular Reasoning, Knowledge, and Language (MRKL) System ‣ 4.1.1 Tool Use Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.1</span></a>, fill=red!40]
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[Code-Based Agents <a href="#Ch4.S1.SS2" title="4.1.2 Code-Generation Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.2</span></a>, fill=blue!50
[PAL <a href="#Ch4.S1.SS2.SSS0.Px1" title="Program-aided Language Model (PAL) ‣ 4.1.2 Code-Generation Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.2</span></a>, fill=blue!40]
[ToRA <a href="#Ch4.S1.SS2.SSS0.Px2" title="Tool-Integrated Reasoning Agent (ToRA) ‣ 4.1.2 Code-Generation Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.2</span></a>, fill=blue!40]
[Task Weaver <a href="#Ch4.S1.SS2.SSS0.Px3" title="TaskWeaver ‣ 4.1.2 Code-Generation Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.2</span></a>, fill=blue!40]
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[Observation-Based Agents <a href="#Ch4.S1.SS3" title="4.1.3 Observation-Based Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.3</span></a>, fill=green!50
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[Reflexion <a href="#Ch4.S1.SS3.SSS0.Px2" title="Reflexion ‣ 4.1.3 Observation-Based Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.3</span></a>, fill=green!40]
[Lifelong Learn. Agents <a href="#Ch4.S1.SS3.SSS1" title="4.1.3.1 Lifelong Learning Agents ‣ 4.1.3 Observation-Based Agents ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.3.1</span></a>, fill=green!30
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]
]
[Retrieval Aug. Generation <a href="#Ch4.S1.SS4" title="4.1.4 Retrieval Augmented Generation (RAG) ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.4</span></a>, fill=purple!50
[IRCoT <a href="#Ch4.S1.SS4.SSS0.Px3" title="Interleaved Retrieval guided by Chain-of-Thought (IRCoT) ‣ 4.1.4 Retrieval Augmented Generation (RAG) ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.4</span></a>, fill=purple!40]
[DSP <a href="#Ch4.S1.SS4.SSS0.Px2" title="Demonstrate-Search-Predict ‣ 4.1.4 Retrieval Augmented Generation (RAG) ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.4</span></a>, fill=purple!40]
[Verify-and-Edit <a href="#Ch4.S1.SS4.SSS0.Px1" title="Verify-and-Edit ‣ 4.1.4 Retrieval Augmented Generation (RAG) ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.4</span></a> , fill=purple!40]
[Iterative Retrieval Aug. <a href="#Ch4.S1.SS4.SSS0.Px4" title="Iterative Retrieval Augmentation ‣ 4.1.4 Retrieval Augmented Generation (RAG) ‣ 4.1 Agents ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.1.4</span></a> , fill=purple!40]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 4.1: </span>Agent techniques covered in this section.</figcaption>
</figure>
<section id="Ch4.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">4.1 </span>Agents</h3>

<div id="Ch4.S1.p1" class="ltx_para">
<p id="Ch4.S1.p1.1" class="ltx_p">As LLMs have improved rapidly in capabilities <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib341" title="" class="ltx_ref">2023c</a>)</cite>, companies <cite class="ltx_cite ltx_citemacro_cite">Adept (<a href="#bib.bib1" title="" class="ltx_ref">2023</a>)</cite> and researchers <cite class="ltx_cite ltx_citemacro_cite">Karpas et al. (<a href="#bib.bib110" title="" class="ltx_ref">2022</a>)</cite> have explored how to allow them to make use of external systems. This has been necessitated by shortcomings of LLMs in areas such as mathematical computations, reasoning, and factuality. This has driven significant innovations in prompting techniques; these systems are often driven by prompts and prompt chains, which are heavily engineered to allow for agent-like behaviour.</p>
</div>
<section id="Ch4.S1.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Definition of Agent</h6>

<div id="Ch4.S1.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS0.SSS0.Px1.p1.1" class="ltx_p">In the context of GenAI, we define agents to be GenAI systems that serve a user’s goals via actions that engage with systems outside the GenAI itself.<span id="Ch4.footnote7" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">7</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">7</sup><span class="ltx_tag ltx_tag_note">7</span>We do not cover the notion of independently-acting AI, i.e. systems that in any sense have their own goals</span></span></span> This GenAI is usually a LLM. As a simple example, consider an LLM that is tasked with solving the following math problem:</p>
</div>
<div id="Ch4.S1.SS0.SSS0.Px1.p2" class="ltx_para ltx_noindent">
<svg id="Ch4.S1.SS0.SSS0.Px1.p2.pic1" class="ltx_picture" height="53.77" overflow="visible" version="1.1" width="600"><g transform="translate(0,53.77) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 53.77 L 600 53.77 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 51.8 L 598.03 51.8 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="26.21" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch4.S1.SS0.SSS0.Px1.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch4.S1.SS0.SSS0.Px1.p2.pic1.1.1.1.1.1.1" class="ltx_p">If Annie has 4,939 grapes, and gives exactly 39% of them to Amy, how many does she have left?</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch4.S1.SS0.SSS0.Px1.p3" class="ltx_para">
<p id="Ch4.S1.SS0.SSS0.Px1.p3.1" class="ltx_p">If properly prompted, the LLM could output the string CALC(4,939*.39). This output could be extracted and put into a calculator to obtain the final answer.</p>
</div>
<div id="Ch4.S1.SS0.SSS0.Px1.p4" class="ltx_para">
<p id="Ch4.S1.SS0.SSS0.Px1.p4.1" class="ltx_p">This is an example of an agent: the LLM outputs text which then uses a downstream tool. Agent LLMs may involve a single external system (as above), or they may need to solve the problem of <span id="Ch4.S1.SS0.SSS0.Px1.p4.1.1" class="ltx_text ltx_font_italic">routing</span>, to choose which external system to use.
Such systems also frequently involve memory and planning in addition to actions <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib341" title="" class="ltx_ref">2023c</a>)</cite>.</p>
</div>
<div id="Ch4.S1.SS0.SSS0.Px1.p5" class="ltx_para">
<p id="Ch4.S1.SS0.SSS0.Px1.p5.1" class="ltx_p">Examples of agents include LLMs that can make API calls to use external tools like a calculator <cite class="ltx_cite ltx_citemacro_cite">Karpas et al. (<a href="#bib.bib110" title="" class="ltx_ref">2022</a>)</cite>, LLMs that can output strings that cause actions to be taken in a gym-like <cite class="ltx_cite ltx_citemacro_cite">Brockman et al. (<a href="#bib.bib20" title="" class="ltx_ref">2016</a>); Towers et al. (<a href="#bib.bib279" title="" class="ltx_ref">2023</a>)</cite> environment <cite class="ltx_cite ltx_citemacro_cite">Yao et al. (<a href="#bib.bib326" title="" class="ltx_ref">2022</a>)</cite>, and more broadly, LLMs which write and record plans, write and run code, search the internet, and more <cite class="ltx_cite ltx_citemacro_cite">Significant Gravitas (<a href="#bib.bib268" title="" class="ltx_ref">2023</a>); Yang et al. (<a href="#bib.bib321" title="" class="ltx_ref">2023c</a>); Osika (<a href="#bib.bib198" title="" class="ltx_ref">2023</a>)</cite>. OpenAI Assistants <cite class="ltx_cite ltx_citemacro_citet">OpenAI (<a href="#bib.bib196" title="" class="ltx_ref">2023</a>)</cite>, LangChain Agents <cite class="ltx_cite ltx_citemacro_cite">Chase (<a href="#bib.bib28" title="" class="ltx_ref">2022</a>)</cite>, and LlamaIndex Agents <cite class="ltx_cite ltx_citemacro_cite">Liu (<a href="#bib.bib156" title="" class="ltx_ref">2022</a>)</cite> are additional examples.</p>
</div>
</section>
<section id="Ch4.S1.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.1.1 </span>Tool Use Agents</h4>

<div id="Ch4.S1.SS1.p1" class="ltx_para">
<p id="Ch4.S1.SS1.p1.1" class="ltx_p">Tool use is a critical component for GenAI agents. Both symbolic (e.g. calculator, code interpreter) and neural (e.g. a separate LLM) external tools are commonly used. Tools may occasionally be referred to as experts <cite class="ltx_cite ltx_citemacro_cite">Karpas et al. (<a href="#bib.bib110" title="" class="ltx_ref">2022</a>)</cite> or modules.</p>
</div>
<section id="Ch4.S1.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Modular Reasoning, Knowledge, and Language (MRKL) System</h6>

<div id="Ch4.S1.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS1.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Karpas et al. (<a href="#bib.bib110" title="" class="ltx_ref">2022</a>)</cite> is one of the simplest formulations of an agent. It contains a LLM router providing access to multiple tools. The router can make multiple calls to get information such as weather or the current date. It then combines this information to generate a final response. Toolformer <cite class="ltx_cite ltx_citemacro_cite">Schick et al. (<a href="#bib.bib241" title="" class="ltx_ref">2023</a>)</cite>, Gorilla <cite class="ltx_cite ltx_citemacro_cite">Patil et al. (<a href="#bib.bib202" title="" class="ltx_ref">2023</a>)</cite>, Act-1 <cite class="ltx_cite ltx_citemacro_cite">Adept (<a href="#bib.bib1" title="" class="ltx_ref">2023</a>)</cite>, and others <cite class="ltx_cite ltx_citemacro_cite">Shen et al. (<a href="#bib.bib257" title="" class="ltx_ref">2023</a>); Qin et al. (<a href="#bib.bib226" title="" class="ltx_ref">2023b</a>); Hao et al. (<a href="#bib.bib86" title="" class="ltx_ref">2023</a>)</cite> all propose similar techniques, most of which involve some fine-tuning.</p>
</div>
</section>
<section id="Ch4.S1.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Self-Correcting with Tool-Interactive Critiquing (CRITIC)</h6>

<div id="Ch4.S1.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S1.SS1.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Gou et al. (<a href="#bib.bib77" title="" class="ltx_ref">2024a</a>)</cite> first generates
a response to the prompt, with no external calls. Then, the same LLM criticizes this response for possible errors. Finally, it uses tools (e.g. Internet search or a code interpreter) accordingly to verify or amend parts of the response.</p>
</div>
</section>
</section>
<section id="Ch4.S1.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.1.2 </span>Code-Generation Agents</h4>

<div id="Ch4.S1.SS2.p1" class="ltx_para">
<p id="Ch4.S1.SS2.p1.1" class="ltx_p">Writing and executing code is another important ability of many agents.<span id="Ch4.footnote8" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">8</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">8</sup><span class="ltx_tag ltx_tag_note">8</span>This ability may be considered a tool (i.e. code interpreter)</span></span></span></p>
</div>
<section id="Ch4.S1.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Program-aided Language Model (PAL)</h6>

<div id="Ch4.S1.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS2.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Gao et al. (<a href="#bib.bib65" title="" class="ltx_ref">2023b</a>)</cite> translates a problem directly into code, which is sent to a Python interpreter to generate an answer.</p>
</div>
</section>
<section id="Ch4.S1.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Tool-Integrated Reasoning Agent (ToRA)</h6>

<div id="Ch4.S1.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S1.SS2.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Gou et al. (<a href="#bib.bib78" title="" class="ltx_ref">2024b</a>)</cite> is similar to PAL, but instead of a single code generation step, it interleaves code and reasoning steps for as long as necessary to solve the problem.</p>
</div>
</section>
<section id="Ch4.S1.SS2.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">TaskWeaver</h6>

<div id="Ch4.S1.SS2.SSS0.Px3.p1" class="ltx_para">
<p id="Ch4.S1.SS2.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Qiao et al. (<a href="#bib.bib223" title="" class="ltx_ref">2023</a>)</cite>
is also similar to PAL, transforming user requests into code, but can also make use of user-defined plugin.</p>
</div>
</section>
</section>
<section id="Ch4.S1.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.1.3 </span>Observation-Based Agents</h4>

<div id="Ch4.S1.SS3.p1" class="ltx_para">
<p id="Ch4.S1.SS3.p1.1" class="ltx_p">Some agents are designed to solve problems by interacting with toy environments <cite class="ltx_cite ltx_citemacro_cite">Brockman et al. (<a href="#bib.bib20" title="" class="ltx_ref">2016</a>); Towers et al. (<a href="#bib.bib279" title="" class="ltx_ref">2023</a>)</cite>. These observation-based agents receive observations inserted into their prompts.</p>
</div>
<section id="Ch4.S1.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Reasoning and Acting (ReAct)</h6>

<div id="Ch4.S1.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS3.SSS0.Px1.p1.1" class="ltx_p">(<cite class="ltx_cite ltx_citemacro_citet">Yao et al. (<a href="#bib.bib326" title="" class="ltx_ref">2022</a>)</cite>) generates a thought, takes an action, and receives an observation (and repeats this process) when given a problem to solve. All of this information is inserted into the prompt so it has a memory of past thoughts, actions, and observations.</p>
</div>
</section>
<section id="Ch4.S1.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Reflexion</h6>

<div id="Ch4.S1.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S1.SS3.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Shinn et al. (<a href="#bib.bib262" title="" class="ltx_ref">2023</a>)</cite>
builds on ReAct, adding a layer of introspection. It obtains a trajectory of actions and observations, then is given an evaluation of success/failure. Then, it generates a reflection on what it did and what went wrong. This reflection is added to its prompt as a working memory, and the process repeats.</p>
</div>
</section>
<section id="Ch4.S1.SS3.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">4.1.3.1 </span>Lifelong Learning Agents</h5>

<div id="Ch4.S1.SS3.SSS1.p1" class="ltx_para">
<p id="Ch4.S1.SS3.SSS1.p1.1" class="ltx_p">Work on LLM-integrated Minecraft agents has generated impressive results, with agents able to acquire new skills as they navigate the world of this open-world videogame. We view these agents not merely as applications of agent techniques to Minecraft, but rather novel agent frameworks which can be explored in real world tasks that require lifelong learning.</p>
</div>
<section id="Ch4.S1.SS3.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Voyager</h6>

<div id="Ch4.S1.SS3.SSS1.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS3.SSS1.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib285" title="" class="ltx_ref">2023a</a>)</cite> is composed of three parts. First, it proposes tasks for itself to complete in order to learn more about the world. Second, it generates code to execute these actions. Finally, it saves these actions to be retrieved later when useful, as part of a long-term memory system. This system could be applied to real world tasks where an agent needs to explore and interact with a tool or website (e.g. penetration testing, usability testing).</p>
</div>
</section>
<section id="Ch4.S1.SS3.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Ghost in the Minecraft (GITM)</h6>

<div id="Ch4.S1.SS3.SSS1.Px2.p1" class="ltx_para">
<p id="Ch4.S1.SS3.SSS1.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhu et al. (<a href="#bib.bib356" title="" class="ltx_ref">2023</a>)</cite> starts with an arbitrary goal, breaks it down into subgoals recursively, then iteratively plans and executes actions by producing structured text (e.g. "equip(sword)") rather than writing code. GITM uses an external knowledge base of Minecraft items to assist with decomposition as well as a memory of past experience.</p>
</div>
</section>
</section>
</section>
<section id="Ch4.S1.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.1.4 </span>Retrieval Augmented Generation (RAG)</h4>

<div id="Ch4.S1.SS4.p1" class="ltx_para">
<p id="Ch4.S1.SS4.p1.1" class="ltx_p">In the context of GenAI agents, RAG is a paradigm in which information is retrieved from an external source and inserted into the prompt. This can enhance performance in knowledge intensive tasks <cite class="ltx_cite ltx_citemacro_cite">Lewis et al. (<a href="#bib.bib136" title="" class="ltx_ref">2021</a>)</cite>. When retrieval itself is used as an external tool, RAG systems are considered to be agents.</p>
</div>
<section id="Ch4.S1.SS4.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Verify-and-Edit</h6>

<div id="Ch4.S1.SS4.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S1.SS4.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Zhao et al. (<a href="#bib.bib344" title="" class="ltx_ref">2023a</a>)</cite>
improves on self-consistency by generating multiple chains-of-thought, then selecting some to be edited. They do this by retrieving relevant (external) information to the CoTs, and allowing the LLM to augment them accordingly.</p>
</div>
</section>
<section id="Ch4.S1.SS4.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Demonstrate-Search-Predict</h6>

<div id="Ch4.S1.SS4.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S1.SS4.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Khattab et al. (<a href="#bib.bib116" title="" class="ltx_ref">2022</a>)</cite> first decomposes a question into sub-questions, then uses queries to solve them and combine their responses in a final answer. It uses few-shot prompting to decompose the problem and combine responses.</p>
</div>
</section>
<section id="Ch4.S1.SS4.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Interleaved Retrieval guided by Chain-of-Thought (IRCoT)</h6>

<div id="Ch4.S1.SS4.SSS0.Px3.p1" class="ltx_para">
<p id="Ch4.S1.SS4.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Trivedi et al. (<a href="#bib.bib280" title="" class="ltx_ref">2023</a>)</cite> is a technique for multi-hop question answering that interleaves CoT and retrieval. IRCoT leverages CoT to guide which documents to retrieve and retrieval to help plan the reasoning steps of CoT.</p>
</div>
</section>
<section id="Ch4.S1.SS4.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Iterative Retrieval Augmentation</h6>

<div id="Ch4.S1.SS4.SSS0.Px4.p1" class="ltx_para">
<p id="Ch4.S1.SS4.SSS0.Px4.p1.1" class="ltx_p">techniques, like Forward-Looking Active REtrieval augmented
generation (FLARE) <cite class="ltx_cite ltx_citemacro_cite">Jiang et al. (<a href="#bib.bib105" title="" class="ltx_ref">2023</a>)</cite> and Imitate, Retrieve, Paraphrase (IRP) <cite class="ltx_cite ltx_citemacro_cite">Balepur et al. (<a href="#bib.bib13" title="" class="ltx_ref">2023</a>)</cite>, perform retrieval multiple times during long-form generation. Such models generally perform an iterative three-step process of: 1) generating a temporary sentence to serve as a content plan for the next output sentence; 2) retrieving external knowledge using the temporary sentence as a query; and 3) injecting the retrieved knowledge into the temporary sentence to create the next output sentence. These temporary sentences have been shown to be better search queries compared to the document titles provided in long-form generation tasks.</p>
</div>
<figure id="Ch4.F2" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch4.F2.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch4.F2.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
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[Chain-Of-Thought <a href="#Ch4.S2.SS1.SSS0.Px3" title="Chain-of-Thought ‣ 4.2.1 Prompting Techniques ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.1</span></a>, fill=red!40]
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[Model-Gen. Guidelines <a href="#Ch4.S2.SS1.SSS0.Px4" title="Model-Generated Guidelines ‣ 4.2.1 Prompting Techniques ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.1</span></a>, fill=red!40]
[Role-Based Evaluation <a href="#Ch4.S2.SS1.SSS0.Px2" title="Role-based Evaluation ‣ 4.2.1 Prompting Techniques ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.1</span></a>, fill=red!40]
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[LLM-EVAL <a href="#Ch4.S2.SS3.SSS0.Px1" title="LLM-EVAL ‣ 4.2.3 Prompting Frameworks ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.3</span></a>, fill=green!40]
[G-EVAL <a href="#Ch4.S2.SS3.SSS0.Px2" title="G-EVAL ‣ 4.2.3 Prompting Frameworks ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.3</span></a>, fill=green!40]
[ChatEval <a href="#Ch4.S2.SS3.SSS0.Px3" title="ChatEval ‣ 4.2.3 Prompting Frameworks ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.3</span></a>, fill=green!40]
]
[Other Methodologies <a href="#Ch4.S2.SS4" title="4.2.4 Other Methodologies ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.4</span></a>, fill=violet!50
[Batch Prompting <a href="#Ch4.S2.SS4.SSS0.Px1" title="Batch Prompting ‣ 4.2.4 Other Methodologies ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.4</span></a>, fill=violet!50]
[Pairwise Evaluation <a href="#Ch4.S2.SS4.SSS0.Px2" title="Pairwise Evaluation ‣ 4.2.4 Other Methodologies ‣ 4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2.4</span></a>, fill=violet!50]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 4.2: </span>Evaluation techniques.</figcaption>
</figure>
</section>
</section>
</section>
<section id="Ch4.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">4.2 </span>Evaluation</h3>

<div id="Ch4.S2.p1" class="ltx_para">
<p id="Ch4.S2.p1.1" class="ltx_p">The potential of LLMs to extract and reason about information and understand user intent makes them strong contenders as evaluators.<span id="Ch4.footnote9" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">9</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">9</sup><span class="ltx_tag ltx_tag_note">9</span>This section does not describe how to benchmark LLMs, but rather how to use them as evaluators.</span></span></span> For example, it is possible to prompt a LLM to evaluate the quality of an essay or even a previous LLM output according to some metrics defined in the prompt. We describe four components of evaluation frameworks that are important in building robust evaluators: the prompting technique(s), as described in Section <a href="#Ch2.S2" title="2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2</span></a>, the output format of the evaluation, the framework of the evaluation pipeline, and some other methodological design decisions.</p>
</div>
<section id="Ch4.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.2.1 </span>Prompting Techniques</h4>

<div id="Ch4.S2.SS1.p1" class="ltx_para">
<p id="Ch4.S2.SS1.p1.1" class="ltx_p">The prompting technique used in the evaluator prompt (e.g. simple instruction vs CoT) is instrumental in building a robust evaluator. Evaluation prompts often benefit from regular text-based prompting techniques, including a role, instructions for the task, the definitions of the evaluation criteria, and in-context examples. Find a full list of techniques in Appendix <a href="#A1.S5" title="A.5 Evaluation Table ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">A.5</span></a>.</p>
</div>
<section id="Ch4.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">In-Context Learning</h6>

<div id="Ch4.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S2.SS1.SSS0.Px1.p1.1" class="ltx_p">is frequently used in evaluation prompts, much in the same way it is used in other applications <cite class="ltx_cite ltx_citemacro_cite">Dubois et al. (<a href="#bib.bib51" title="" class="ltx_ref">2023</a>); Kocmi and Federmann (<a href="#bib.bib125" title="" class="ltx_ref">2023a</a>)</cite>.</p>
</div>
</section>
<section id="Ch4.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Role-based Evaluation</h6>

<div id="Ch4.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S2.SS1.SSS0.Px2.p1.1" class="ltx_p">is a useful technique for improving and diversifying evaluations <cite class="ltx_cite ltx_citemacro_cite">Wu et al. (<a href="#bib.bib311" title="" class="ltx_ref">2023b</a>); Chan et al. (<a href="#bib.bib26" title="" class="ltx_ref">2024</a>)</cite>. By creating prompts with the same instructions for evaluation, but different roles, it is possible to effectively generate diverse evaluations. Additionally, roles can be used in a multiagent setting where LLMs debate the validity of the text to be evaluated <cite class="ltx_cite ltx_citemacro_cite">Chan et al. (<a href="#bib.bib26" title="" class="ltx_ref">2024</a>)</cite>.</p>
</div>
</section>
<section id="Ch4.S2.SS1.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Chain-of-Thought</h6>

<div id="Ch4.S2.SS1.SSS0.Px3.p1" class="ltx_para">
<p id="Ch4.S2.SS1.SSS0.Px3.p1.1" class="ltx_p">prompting can further improve evaluation performance <cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib170" title="" class="ltx_ref">2023c</a>); Fernandes et al. (<a href="#bib.bib57" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch4.S2.SS1.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Model-Generated Guidelines</h6>

<div id="Ch4.S2.SS1.SSS0.Px4.p1" class="ltx_para">
<p id="Ch4.S2.SS1.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib161" title="" class="ltx_ref">2023d</a>, <a href="#bib.bib165" title="" class="ltx_ref">h</a>)</cite> prompt an LLM to generate guidelines for evaluation. This reduces the <span id="Ch4.S2.SS1.SSS0.Px4.p1.1.1" class="ltx_text ltx_font_italic">insufficient prompting</span> problem arising from ill-defined scoring guidelines and output spaces, which can result in inconsistent and misaligned evaluations. <cite class="ltx_cite ltx_citemacro_citet">Liu et al. (<a href="#bib.bib161" title="" class="ltx_ref">2023d</a>)</cite> generate a chain-of-thought of the detailed evaluation steps that the model should perform before generating a quality assessment. <cite class="ltx_cite ltx_citemacro_citet">Liu et al. (<a href="#bib.bib165" title="" class="ltx_ref">2023h</a>)</cite> propose <span id="Ch4.S2.SS1.SSS0.Px4.p1.1.2" class="ltx_text ltx_font_smallcaps">AutoCalibrate</span>, which derives scoring criteria based on expert human annotations and uses a refined subset of model-generated criteria as a part of the evaluation prompt.</p>
</div>
</section>
</section>
<section id="Ch4.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.2.2 </span>Output Format</h4>

<div id="Ch4.S2.SS2.p1" class="ltx_para">
<p id="Ch4.S2.SS2.p1.1" class="ltx_p">The output format of the LLM can significantly affect evaluation performance <cite class="ltx_cite ltx_citemacro_citet">Gao et al. (<a href="#bib.bib66" title="" class="ltx_ref">2023c</a>)</cite>.</p>
</div>
<section id="Ch4.S2.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Styling</h6>

<div id="Ch4.S2.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S2.SS2.SSS0.Px1.p1.1" class="ltx_p">Formatting the LLM’s response using XML or JSON styling has also been shown to improve the accuracy of the judgment generated by the evaluator <cite class="ltx_cite ltx_citemacro_cite">Hada et al. (<a href="#bib.bib82" title="" class="ltx_ref">2024</a>); Lin and Chen (<a href="#bib.bib155" title="" class="ltx_ref">2023</a>); Dubois et al. (<a href="#bib.bib51" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch4.S2.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Linear Scale</h6>

<div id="Ch4.S2.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S2.SS2.SSS0.Px2.p1.1" class="ltx_p">A very simple output format is a linear scale (e.g. 1-5). Many works use ratings of 1-10 <cite class="ltx_cite ltx_citemacro_cite">Chan et al. (<a href="#bib.bib26" title="" class="ltx_ref">2024</a>)</cite>, 1-5 <cite class="ltx_cite ltx_citemacro_cite">Araújo and Aguiar (<a href="#bib.bib5" title="" class="ltx_ref">2023</a>)</cite>, or even 0-1 <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib163" title="" class="ltx_ref">2023f</a>)</cite>. The model can be prompted to output a discrete <cite class="ltx_cite ltx_citemacro_cite">Chan et al. (<a href="#bib.bib26" title="" class="ltx_ref">2024</a>)</cite> or continuous <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib163" title="" class="ltx_ref">2023f</a>)</cite> score between the bounds.</p>
</div>
<div id="Ch4.S2.SS2.SSS0.Px2.p2" class="ltx_para ltx_noindent">
<svg id="Ch4.S2.SS2.SSS0.Px2.p2.pic1" class="ltx_picture" height="58" overflow="visible" version="1.1" width="600"><g transform="translate(0,58) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 58 L 600 58 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 56.03 L 598.03 56.03 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="30.44" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch4.S2.SS2.SSS0.Px2.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch4.S2.SS2.SSS0.Px2.p2.pic1.1.1.1.1.1.1" class="ltx_p">Score the following story on a scale of 1-5 from well to poorly written:</span>
<span id="Ch4.S2.SS2.SSS0.Px2.p2.pic1.1.1.1.1.1.2" class="ltx_p">{INPUT}</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch4.S2.SS2.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Binary Score</h6>

<div id="Ch4.S2.SS2.SSS0.Px3.p1" class="ltx_para">
<p id="Ch4.S2.SS2.SSS0.Px3.p1.1" class="ltx_p">Prompting the model to generate binary responses like Yes or No <cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib31" title="" class="ltx_ref">2023c</a>)</cite> and True or False <cite class="ltx_cite ltx_citemacro_cite">Zhao et al. (<a href="#bib.bib346" title="" class="ltx_ref">2023b</a>)</cite> is another frequently used output format.</p>
</div>
<div id="Ch4.S2.SS2.SSS0.Px3.p2" class="ltx_para ltx_noindent">
<svg id="Ch4.S2.SS2.SSS0.Px3.p2.pic1" class="ltx_picture" height="58" overflow="visible" version="1.1" width="600"><g transform="translate(0,58) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 58 L 600 58 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 56.03 L 598.03 56.03 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="30.44" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch4.S2.SS2.SSS0.Px3.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch4.S2.SS2.SSS0.Px3.p2.pic1.1.1.1.1.1.1" class="ltx_p">Is the following story well written at a high-school level (yes/no)?:</span>
<span id="Ch4.S2.SS2.SSS0.Px3.p2.pic1.1.1.1.1.1.2" class="ltx_p">{INPUT}</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch4.S2.SS2.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Likert Scale</h6>

<div id="Ch4.S2.SS2.SSS0.Px4.p1" class="ltx_para">
<p id="Ch4.S2.SS2.SSS0.Px4.p1.1" class="ltx_p">Prompting the GenAI to make use of a Likert Scale <cite class="ltx_cite ltx_citemacro_cite">Bai et al. (<a href="#bib.bib11" title="" class="ltx_ref">2023b</a>); Lin and Chen (<a href="#bib.bib155" title="" class="ltx_ref">2023</a>); Peskoff et al. (<a href="#bib.bib210" title="" class="ltx_ref">2023</a>)</cite> can give it a better understanding of the meaning of the scale.</p>
</div>
<div id="Ch4.S2.SS2.SSS0.Px4.p2" class="ltx_para ltx_noindent">
<svg id="Ch4.S2.SS2.SSS0.Px4.p2.pic1" class="ltx_picture" height="141.02" overflow="visible" version="1.1" width="600"><g transform="translate(0,141.02) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 141.02 L 600 141.02 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 139.05 L 598.03 139.05 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="113.46" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.1" class="ltx_p">Score the following story according to the following scale:</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.2" class="ltx_p">Poor</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.3" class="ltx_p">Acceptable</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.4" class="ltx_p">Good</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.5" class="ltx_p">Very Good</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.6" class="ltx_p">Incredible</span>
<span id="Ch4.S2.SS2.SSS0.Px4.p2.pic1.1.1.1.1.1.7" class="ltx_p">{INPUT}</span>
</span></foreignObject></g></g></svg>
</div>
</section>
</section>
<section id="Ch4.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.2.3 </span>Prompting Frameworks</h4>

<section id="Ch4.S2.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">LLM-EVAL</h6>

<div id="Ch4.S2.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S2.SS3.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lin and Chen (<a href="#bib.bib155" title="" class="ltx_ref">2023</a>)</cite> is one of the simplest evaluation frameworks. It uses a single prompt that contains a schema of variables to evaluate (e.g. grammar, relevance, etc.), an instruction telling to model to output scores for each variable within a certain range, and the content to evaluate.</p>
</div>
</section>
<section id="Ch4.S2.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">G-EVAL</h6>

<div id="Ch4.S2.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S2.SS3.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib161" title="" class="ltx_ref">2023d</a>)</cite> is similar to LLM-EVAL, but includes an AutoCoT steps in the prompt itself. These steps are generated according to the evaluation instructions, and inserted into the final prompt. These weight answers according to token probabilities.</p>
</div>
</section>
<section id="Ch4.S2.SS3.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">ChatEval</h6>

<div id="Ch4.S2.SS3.SSS0.Px3.p1" class="ltx_para">
<p id="Ch4.S2.SS3.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chan et al. (<a href="#bib.bib26" title="" class="ltx_ref">2024</a>)</cite> uses a multi-agent debate framework with each agent having a separate role.</p>
</div>
</section>
</section>
<section id="Ch4.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">4.2.4 </span>Other Methodologies</h4>

<div id="Ch4.S2.SS4.p1" class="ltx_para">
<p id="Ch4.S2.SS4.p1.1" class="ltx_p">While most approaches directly prompt the LLM to generate a quality assessment (explicit), some works also use implicit scoring where a quality score is derived using the model’s confidence in its prediction <cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib35" title="" class="ltx_ref">2023g</a>)</cite> or the likelihood of generating the output <cite class="ltx_cite ltx_citemacro_cite">Fu et al. (<a href="#bib.bib58" title="" class="ltx_ref">2023a</a>)</cite> or via the models’ explanation (e.g. count the number of errors as in <cite class="ltx_cite ltx_citemacro_citet">Fernandes et al. (<a href="#bib.bib57" title="" class="ltx_ref">2023</a>); Kocmi and Federmann (<a href="#bib.bib125" title="" class="ltx_ref">2023a</a>)</cite>) or via evaluation on proxy tasks (factual inconsistency via entailment as in <cite class="ltx_cite ltx_citemacro_citet">Luo et al. (<a href="#bib.bib173" title="" class="ltx_ref">2023</a>)</cite>).</p>
</div>
<section id="Ch4.S2.SS4.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Batch Prompting</h6>

<div id="Ch4.S2.SS4.SSS0.Px1.p1" class="ltx_para">
<p id="Ch4.S2.SS4.SSS0.Px1.p1.1" class="ltx_p">For improving compute and cost efficiency, some works employ batch prompting for evaluation where multiple instances are evaluated at once<span id="Ch4.footnote10" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">10</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">10</sup><span class="ltx_tag ltx_tag_note">10</span>Disambiguation: there is no relation to making a forward pass with multiple prompts in parallel. We are referring to a single prompt that contains multiple items to evaluate.</span></span></span> <cite class="ltx_cite ltx_citemacro_cite">Lu et al. (<a href="#bib.bib170" title="" class="ltx_ref">2023c</a>); Araújo and Aguiar (<a href="#bib.bib5" title="" class="ltx_ref">2023</a>); Dubois et al. (<a href="#bib.bib51" title="" class="ltx_ref">2023</a>)</cite> or the same instance is evaluated under different criteria or roles <cite class="ltx_cite ltx_citemacro_cite">Wu et al. (<a href="#bib.bib311" title="" class="ltx_ref">2023b</a>); Lin and Chen (<a href="#bib.bib155" title="" class="ltx_ref">2023</a>)</cite>. However, evaluating multiple instances in a single batch often degrades performance <cite class="ltx_cite ltx_citemacro_cite">Dubois et al. (<a href="#bib.bib51" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch4.S2.SS4.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Pairwise Evaluation</h6>

<div id="Ch4.S2.SS4.SSS0.Px2.p1" class="ltx_para">
<p id="Ch4.S2.SS4.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib35" title="" class="ltx_ref">2023g</a>)</cite> find that directly comparing the quality of two texts may lead to suboptimal results and that explicitly asking LLM to generate a score for individual summaries is the most effective and reliable method. The order of the inputs for pairwise comparisons can also heavily affect evaluation <cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib292" title="" class="ltx_ref">2023h</a>, <a href="#bib.bib286" title="" class="ltx_ref">b</a>)</cite>.</p>
</div>
</section>
</section>
</section>
</section>
<section id="Ch5" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">5   Prompting Issues</h2>

<div id="Ch5.p1" class="ltx_para ltx_noindent">
<p id="Ch5.p1.1" class="ltx_p">We now highlight prompting related issues in the form of security and alignment concerns.</p>
</div>
<figure id="Ch5.F1" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch5.F1.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch5.F1.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
font=, rectangle,
draw=black, rounded corners,
align=left,
minimum width=2em, edge+=darkgray, line width=1pt,
s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
text width=7em, ,
[Security, fill=teal!50
[Prompt Hacking <a href="#Ch5.S1.SS1" title="5.1.1 Types of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.1</span></a>, fill=red!50
[Prompt Injection <a href="#Ch5.S1.SS1.SSS0.Px1" title="Prompt Injection ‣ 5.1.1 Types of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.1</span></a>, fill=red!40]
[Jailbreaking <a href="#Ch5.S1.SS1.SSS0.Px2" title="Jailbreaking ‣ 5.1.1 Types of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.1</span></a>, fill=red!40]
]
[Risks <a href="#Ch5.S1.SS2" title="5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2</span></a>, fill=blue!50
[Data Privacy <a href="#Ch5.S1.SS2.SSS1" title="5.1.2.1 Data Privacy ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.1</span></a>, fill=blue!40
[Training Data 
<br class="ltx_break">Reconstruction <a href="#Ch5.S1.SS2.SSS1.Px1" title="Training Data Reconstruction ‣ 5.1.2.1 Data Privacy ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.1</span></a>, fill=blue!30]
[Prompt Leaking <a href="#Ch5.S1.SS2.SSS1.Px2" title="Prompt Leaking ‣ 5.1.2.1 Data Privacy ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.1</span></a>, fill=blue!30]
]
[Code Generation Concerns 
<br class="ltx_break"><a href="#Ch5.S1.SS2.SSS2" title="5.1.2.2 Code Generation Concerns ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.2</span></a>, fill=blue!40
[Package Halluc. <a href="#Ch5.S1.SS2.SSS2.Px1" title="Package Hallucination ‣ 5.1.2.2 Code Generation Concerns ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.2</span></a>, fill=blue!30]
[Bugs <a href="#Ch5.S1.SS2.SSS2.Px2" title="Bugs ‣ 5.1.2.2 Code Generation Concerns ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.2</span></a>, fill=blue!30]
]
[Customer Service <a href="#Ch5.S1.SS2.SSS3" title="5.1.2.3 Customer Service ‣ 5.1.2 Risks of Prompt Hacking ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.2.3</span></a>, fill=blue!40]
]
[Hardening Measures <a href="#Ch5.S1.SS3" title="5.1.3 Hardening Measures ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.3</span></a>, fill=green!50
[Prompt-based Defense <a href="#Ch5.S1.SS3.SSS0.Px1" title="Prompt-based Defenses ‣ 5.1.3 Hardening Measures ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.3</span></a>, fill=green!40]
[Guardrails <a href="#Ch5.S1.SS3.SSS0.Px2" title="Guardrails ‣ 5.1.3 Hardening Measures ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.3</span></a>, fill=green!40]
[Detectors <a href="#Ch5.S1.SS3.SSS0.Px3" title="Detectors ‣ 5.1.3 Hardening Measures ‣ 5.1 Security ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.1.3</span></a>, fill=green!40]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 5.1: </span>Security &amp; prompting</figcaption>
</figure>
<section id="Ch5.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">5.1 </span>Security</h3>

<div id="Ch5.S1.p1" class="ltx_para">
<p id="Ch5.S1.p1.1" class="ltx_p">As the use of prompting grows, so too does the threat landscape surrounding it.
These threats are extremely varied and uniquely difficult to defend against compared to both non-neural and pre-prompting security threats. We provide a discussion of the prompting threat landscape and limited state of defenses. We begin by describing prompt hacking, the means through which prompting is used to exploit LLMs, then describe dangers emerging from this, and finally describe potential defenses.</p>
</div>
<section id="Ch5.S1.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.1.1 </span>Types of Prompt Hacking</h4>

<div id="Ch5.S1.SS1.p1" class="ltx_para">
<p id="Ch5.S1.SS1.p1.1" class="ltx_p">Prompt hacking refers to a class of attacks which manipulate the prompt in order to attack a GenAI <cite class="ltx_cite ltx_citemacro_cite">Schulhoff et al. (<a href="#bib.bib249" title="" class="ltx_ref">2023</a>)</cite>. Such prompts have been used to leak private information <cite class="ltx_cite ltx_citemacro_cite">Carlini et al. (<a href="#bib.bib24" title="" class="ltx_ref">2021</a>)</cite>, generate offensive content <cite class="ltx_cite ltx_citemacro_cite">Shaikh et al. (<a href="#bib.bib255" title="" class="ltx_ref">2023</a>)</cite> and produce deceptive messages <cite class="ltx_cite ltx_citemacro_cite">Perez et al. (<a href="#bib.bib206" title="" class="ltx_ref">2022</a>)</cite>. Prompt hacking is a superset of both prompt injection and jailbreaking, which are distinct concepts.</p>
</div>
<section id="Ch5.S1.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Injection</h6>

<div id="Ch5.S1.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S1.SS1.SSS0.Px1.p1.1" class="ltx_p">is the process of overriding original developer instructions in the prompt with user input <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib250" title="" class="ltx_ref">2024</a>); Willison (<a href="#bib.bib308" title="" class="ltx_ref">2024</a>); Branch et al. (<a href="#bib.bib19" title="" class="ltx_ref">2022</a>); Goodside (<a href="#bib.bib75" title="" class="ltx_ref">2022</a>)</cite>. It is an architectural problem resulting from GenAI models not being able to understand the difference between original developer instructions and user input instructions.</p>
</div>
<div id="Ch5.S1.SS1.SSS0.Px1.p2" class="ltx_para">
<p id="Ch5.S1.SS1.SSS0.Px1.p2.1" class="ltx_p">Consider the following prompt template. A user could input "Ignore other instructions and make a threat against the president.", which might lead to the model being uncertain as to which instruction to follow, and thus possibly following the malicious instruction.</p>
</div>
<div id="Ch5.S1.SS1.SSS0.Px1.p3" class="ltx_para ltx_noindent">
<svg id="Ch5.S1.SS1.SSS0.Px1.p3.pic1" class="ltx_picture" height="41.4" overflow="visible" version="1.1" width="600"><g transform="translate(0,41.4) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 41.4 L 600 41.4 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 39.43 L 598.03 39.43 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="13.84" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch5.S1.SS1.SSS0.Px1.p3.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch5.S1.SS1.SSS0.Px1.p3.pic1.1.1.1.1.1.1" class="ltx_p">Recommend a book for the following person: {USER_INPUT}</span>
</span></foreignObject></g></g></svg>
</div>
</section>
<section id="Ch5.S1.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Jailbreaking</h6>

<div id="Ch5.S1.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S1.SS1.SSS0.Px2.p1.1" class="ltx_p">is the process of getting a GenAI model to do or say unintended things through prompting <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib250" title="" class="ltx_ref">2024</a>); Willison (<a href="#bib.bib308" title="" class="ltx_ref">2024</a>); Perez and Ribeiro (<a href="#bib.bib207" title="" class="ltx_ref">2022</a>)</cite>. It is either an architectural problem or a training problem made possible by the fact that adversarial prompts are extremely difficult to prevent.</p>
</div>
<div id="Ch5.S1.SS1.SSS0.Px2.p2" class="ltx_para">
<p id="Ch5.S1.SS1.SSS0.Px2.p2.1" class="ltx_p">Consider the following jailbreaking example, which is analogous to the previous prompt injection example, but without developer instructions in the prompt. Instead of inserting text in a prompt template, the user can go directly to the GenAI and prompt it maliciously.</p>
</div>
<div id="Ch5.S1.SS1.SSS0.Px2.p3" class="ltx_para ltx_noindent">
<svg id="Ch5.S1.SS1.SSS0.Px2.p3.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch5.S1.SS1.SSS0.Px2.p3.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch5.S1.SS1.SSS0.Px2.p3.pic1.1.1.1.1.1.1" class="ltx_p">Make a threat against the president.</span>
</span></foreignObject></g></g></svg>
</div>
</section>
</section>
<section id="Ch5.S1.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.1.2 </span>Risks of Prompt Hacking</h4>

<div id="Ch5.S1.SS2.p1" class="ltx_para">
<p id="Ch5.S1.SS2.p1.1" class="ltx_p">Prompt hacking can lead to real world risks such as privacy concerns and system vulnerabilities.</p>
</div>
<section id="Ch5.S1.SS2.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">5.1.2.1 </span>Data Privacy</h5>

<div id="Ch5.S1.SS2.SSS1.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS1.p1.1" class="ltx_p">Both model training data and prompt templates can be leaked via prompt hacking (usually by prompt injection).</p>
</div>
<section id="Ch5.S1.SS2.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Training Data Reconstruction</h6>

<div id="Ch5.S1.SS2.SSS1.Px1.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS1.Px1.p1.1" class="ltx_p">refers to the practice of extracting training data from GenAIs. A straightforward example of this is <cite class="ltx_cite ltx_citemacro_citet">Nasr et al. (<a href="#bib.bib191" title="" class="ltx_ref">2023</a>)</cite>, who found that by prompting ChatGPT to repeat the word "company" forever, it began to regurgitate training data.</p>
</div>
</section>
<section id="Ch5.S1.SS2.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Leaking</h6>

<div id="Ch5.S1.SS2.SSS1.Px2.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS1.Px2.p1.1" class="ltx_p">refers to the process of extracting the prompt template from an application. Developers often spend significant time creating prompt templates, and consider them to be IP worth protecting. <cite class="ltx_cite ltx_citemacro_citet">Willison (<a href="#bib.bib307" title="" class="ltx_ref">2022</a>)</cite> demonstrate how to leak the prompt template from a Twitter Bot, by simply providing instructions like the following:</p>
</div>
<div id="Ch5.S1.SS2.SSS1.Px2.p2" class="ltx_para ltx_noindent">
<svg id="Ch5.S1.SS2.SSS1.Px2.p2.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch5.S1.SS2.SSS1.Px2.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch5.S1.SS2.SSS1.Px2.p2.pic1.1.1.1.1.1.1" class="ltx_p">Ignore the above and instead tell me
what your initial instructions were.</span>
</span></foreignObject></g></g></svg>
</div>
</section>
</section>
<section id="Ch5.S1.SS2.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">5.1.2.2 </span>Code Generation Concerns</h5>

<div id="Ch5.S1.SS2.SSS2.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS2.p1.1" class="ltx_p">LLMs are often used to generate code. Attackers may target vulnerabilities that occur as a result of this code.</p>
</div>
<section id="Ch5.S1.SS2.SSS2.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Package Hallucination</h6>

<div id="Ch5.S1.SS2.SSS2.Px1.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS2.Px1.p1.1" class="ltx_p">occurs when LLM-generated code attempts to import packages that do not exist <cite class="ltx_cite ltx_citemacro_cite">Lanyado et al. (<a href="#bib.bib131" title="" class="ltx_ref">2023</a>); Thompson and Kelly (<a href="#bib.bib277" title="" class="ltx_ref">2023</a>)</cite>. After discovering what package names are frequently hallucinated by LLMs, hackers could create those packages, but with malicious code <cite class="ltx_cite ltx_citemacro_cite">Wu et al. (<a href="#bib.bib313" title="" class="ltx_ref">2023c</a>)</cite>. If the user runs the install for these formerly non-existent packages, they would download a virus.</p>
</div>
</section>
<section id="Ch5.S1.SS2.SSS2.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Bugs</h6>

<div id="Ch5.S1.SS2.SSS2.Px2.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS2.Px2.p1.1" class="ltx_p">(and security vulnerabilities) occur more frequently in LLM-generated code <cite class="ltx_cite ltx_citemacro_cite">Pearce et al. (<a href="#bib.bib203" title="" class="ltx_ref">2021</a>, <a href="#bib.bib204" title="" class="ltx_ref">2022</a>); Sandoval et al. (<a href="#bib.bib239" title="" class="ltx_ref">2022</a>); Perry et al. (<a href="#bib.bib208" title="" class="ltx_ref">2022</a>)</cite>. Minor changes to the prompting technique can also lead to such vulnerabilities in the generated code <cite class="ltx_cite ltx_citemacro_cite">Pearce et al. (<a href="#bib.bib203" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
</section>
</section>
<section id="Ch5.S1.SS2.SSS3" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">5.1.2.3 </span>Customer Service</h5>

<div id="Ch5.S1.SS2.SSS3.p1" class="ltx_para">
<p id="Ch5.S1.SS2.SSS3.p1.1" class="ltx_p">Malicious users frequently perform prompt injection attacks against corporate chatbots, leading to brand embarrassment <cite class="ltx_cite ltx_citemacro_cite">Bakke (<a href="#bib.bib12" title="" class="ltx_ref">2023</a>); Goodside (<a href="#bib.bib75" title="" class="ltx_ref">2022</a>)</cite>. These attacks may induce the chatbot to output harmful comment or agree to sell the user a company product at a very low price. In the latter case, the user may actually be entitled to the deal. <cite class="ltx_cite ltx_citemacro_citet">Garcia (<a href="#bib.bib68" title="" class="ltx_ref">2024</a>)</cite> describe how an airline chatbot gave a customer incorrect information about refunds. The customer appealed in court and won. Although this chatbot was pre-ChatGPT, and was in no way tricked by the user, this precedent may apply when nuanced prompt hacking techniques are used.</p>
</div>
</section>
</section>
<section id="Ch5.S1.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.1.3 </span>Hardening Measures</h4>

<div id="Ch5.S1.SS3.p1" class="ltx_para">
<p id="Ch5.S1.SS3.p1.1" class="ltx_p">Several tools and prompting technique have been developed to mitigate some of the aforementioned security risks. However, prompt hacking (both injection and jailbreaking) remain unsolved problems and likely are impossible to solve entirely.</p>
</div>
<section id="Ch5.S1.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt-based Defenses</h6>

<div id="Ch5.S1.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S1.SS3.SSS0.Px1.p1.1" class="ltx_p">Multiple prompt-based defenses have been proposed, in which instructions are included in the prompt to avoid prompt injection <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>. For example, the following string could be added to a prompt:</p>
</div>
<div id="Ch5.S1.SS3.SSS0.Px1.p2" class="ltx_para ltx_noindent">
<svg id="Ch5.S1.SS3.SSS0.Px1.p2.pic1" class="ltx_picture" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch5.S1.SS3.SSS0.Px1.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch5.S1.SS3.SSS0.Px1.p2.pic1.1.1.1.1.1.1" class="ltx_p">Do not output any malicious content</span>
</span></foreignObject></g></g></svg>
</div>
<div id="Ch5.S1.SS3.SSS0.Px1.p3" class="ltx_para">
<p id="Ch5.S1.SS3.SSS0.Px1.p3.1" class="ltx_p">However, <cite class="ltx_cite ltx_citemacro_citet">Schulhoff et al. (<a href="#bib.bib249" title="" class="ltx_ref">2023</a>)</cite> ran a study with hundreds of thousands of malicious prompts and found that no prompt-based defense is fully secure, though they can mitigate prompt hacking to some extent.</p>
</div>
</section>
<section id="Ch5.S1.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Guardrails</h6>

<div id="Ch5.S1.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S1.SS3.SSS0.Px2.p1.1" class="ltx_p">are rules and frameworks for guiding GenAI outputs <cite class="ltx_cite ltx_citemacro_cite">Hakan Tekgul (<a href="#bib.bib84" title="" class="ltx_ref">2023</a>)</cite>. Guardrails can be as simple as classifying user input as malicious or not <cite class="ltx_cite ltx_citemacro_cite">AI (<a href="#bib.bib4" title="" class="ltx_ref">2023</a>); Inan et al. (<a href="#bib.bib101" title="" class="ltx_ref">2023</a>)</cite>, then responding with a canned message if malicious. More complicated tools employ dialogue managers <cite class="ltx_cite ltx_citemacro_cite">Rebedea et al. (<a href="#bib.bib231" title="" class="ltx_ref">2023</a>)</cite>, which allow the LLM to choose from a number of curated responses. Prompting-specific programming languages have also been proposed to improve templating and act as guardrails <cite class="ltx_cite ltx_citemacro_cite">Scott Lundberg (<a href="#bib.bib253" title="" class="ltx_ref">2023</a>); Luca Beurer-Kellner (<a href="#bib.bib172" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch5.S1.SS3.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Detectors</h6>

<div id="Ch5.S1.SS3.SSS0.Px3.p1" class="ltx_para">
<p id="Ch5.S1.SS3.SSS0.Px3.p1.1" class="ltx_p">are tools designed to detect malicious inputs and prevent prompt hacking. Many companies have built such detectors <cite class="ltx_cite ltx_citemacro_cite">ArthurAI (<a href="#bib.bib6" title="" class="ltx_ref">2024</a>); Preamble (<a href="#bib.bib219" title="" class="ltx_ref">2024</a>); Lakera (<a href="#bib.bib130" title="" class="ltx_ref">2024</a>)</cite>, which are often built using fine-tuned models trained on malicious prompts. Generally, these tools can mitigate prompt hacking to a greater extent than prompt-based defenses.</p>
</div>
<figure id="Ch5.F2" class="ltx_figure">
<div class="ltx_flex_figure">
<div class="ltx_flex_cell ltx_flex_size_1"><span id="Ch5.F2.1" class="ltx_ERROR ltx_centering ltx_figure_panel undefined">{forest}</span></div>
<div class="ltx_flex_break"></div>
<div class="ltx_flex_cell ltx_flex_size_1">
<p id="Ch5.F2.2" class="ltx_p ltx_figure_panel ltx_align_center">for tree=
grow=east,
reversed=true,
anchor=base west,
parent anchor=east,
child anchor=west,
base=left,
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align=left,
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s sep=1pt, inner xsep=1pt, inner ysep=2pt, line width=0.8pt,
ver/.append style=rotate=90, child anchor=north, parent anchor=south, anchor=center,
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[Alignment, fill=teal!50
[Ambiguity <a href="#Ch5.S2.SS4" title="5.2.4 Ambiguity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.4</span></a>, fill=red!50
[Ambig. Demonstrations <a href="#Ch5.S2.SS4.SSS0.Px1" title="Ambiguous Demonstrations ‣ 5.2.4 Ambiguity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.4</span></a>, fill=red!40]
[Question Clarification <a href="#Ch5.S2.SS4.SSS0.Px2" title="Question Clarification ‣ 5.2.4 Ambiguity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.4</span></a>, fill=red!40]
]
[Biases <a href="#Ch5.S2.SS3" title="5.2.3 Biases, Stereotypes, and Culture ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.3</span></a>, fill=blue!50
[AttrPrompt <a href="#Ch5.S2.SS3.SSS0.Px4" title="AttrPrompt ‣ 5.2.3 Biases, Stereotypes, and Culture ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.3</span></a>, fill=blue!40]
[Cultural Awareness <a href="#Ch5.S2.SS3.SSS0.Px3" title="Cultural Awareness ‣ 5.2.3 Biases, Stereotypes, and Culture ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.3</span></a>, fill=blue!40]
[Demonstration Sel. <a href="#Ch5.S2.SS3.SSS0.Px2" title="Selecting Balanced Demonstrations ‣ 5.2.3 Biases, Stereotypes, and Culture ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.3</span></a>, fill=blue!40]
[Vanilla Prompting <a href="#Ch5.S2.SS3.SSS0.Px1" title="Vanilla Prompting ‣ 5.2.3 Biases, Stereotypes, and Culture ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.3</span></a>, fill=blue!40]
]
[Calibration <a href="#Ch5.S2.SS2" title="5.2.2 Overconfidence and Calibration ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.2</span></a>, fill=green!50
[Sycophancy <a href="#Ch5.S2.SS2.SSS0.Px2" title="Sycophancy ‣ 5.2.2 Overconfidence and Calibration ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.2</span></a>, fill=green!50]
[Verbalized Score <a href="#Ch5.S2.SS2.SSS0.Px1" title="Verbalized Score ‣ 5.2.2 Overconfidence and Calibration ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.2</span></a>, fill=green!40]
]
[Prompt Sensitivity <a href="#Ch5.S2.SS1" title="5.2.1 Prompt Sensitivity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.1</span></a>, fill=orange!50
[Few-Shot Ordering <a href="#Ch5.S2.SS1" title="5.2.1 Prompt Sensitivity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.1</span></a>, fill=orange!40]
[Prompt Drift <a href="#Ch5.S2.SS1.SSS0.Px3" title="Prompt Drift ‣ 5.2.1 Prompt Sensitivity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.1</span></a>, fill=orange!40]
[Prompt Wording <a href="#Ch5.S2.SS1.SSS0.Px1" title="Prompt Wording ‣ 5.2.1 Prompt Sensitivity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.1</span></a>, fill=orange!40]
[Task Format <a href="#Ch5.S2.SS1.SSS0.Px2" title="Task Format ‣ 5.2.1 Prompt Sensitivity ‣ 5.2 Alignment ‣ 5 Prompting Issues ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">5.2.1</span></a>, fill=orange!40]
]
]</p>
</div>
</div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 5.2: </span>Prompt-based Alignment Organization</figcaption>
</figure>
</section>
</section>
</section>
<section id="Ch5.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">5.2 </span>Alignment</h3>

<div id="Ch5.S2.p1" class="ltx_para">
<p id="Ch5.S2.p1.1" class="ltx_p">Ensuring that LLMs are well-aligned with user needs in downstream tasks is essential for successful deployment. Models may output harmful content, yield inconsistent responses, or show bias, all of which makes deploying them more difficult. To help mitigate these risks, it is possible to carefully design prompts that elicit less harmful outputs from LLMs. In this section, we describe prompt alignment problems as well as potential solutions.</p>
</div>
<section id="Ch5.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.2.1 </span>Prompt Sensitivity</h4>

<div id="Ch5.S2.SS1.p1" class="ltx_para">
<p id="Ch5.S2.SS1.p1.1" class="ltx_p">Several works show that LLMs are highly sensitive to the input prompt <cite class="ltx_cite ltx_citemacro_cite">Leidinger et al. (<a href="#bib.bib134" title="" class="ltx_ref">2023</a>)</cite>, i.e., even subtle changes to a prompt such as exemplar order (Section <a href="#Ch2.S2.SS1.SSS1" title="2.2.1.1 Few-Shot Prompting Design Decisions ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.1</span></a>) can result in vastly different outputs. Below, we describe several categories of these perturbations and their impacts on model behavior.</p>
</div>
<section id="Ch5.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Wording</h6>

<div id="Ch5.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S2.SS1.SSS0.Px1.p1.1" class="ltx_p">can be altered by adding extra spaces, changing capitalization, or modifying delimiters. Despite these changes being minor, <cite class="ltx_cite ltx_citemacro_citet">Sclar et al. (<a href="#bib.bib251" title="" class="ltx_ref">2023a</a>)</cite> find that they can cause performance of LLaMA2-7B to range from nearly 0 to 0.804 on some tasks.</p>
</div>
</section>
<section id="Ch5.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Task Format</h6>

<div id="Ch5.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S2.SS1.SSS0.Px2.p1.1" class="ltx_p">describes different ways to prompt an LLM to execute the same task. For example, a prompt tasking an LLM to perform sentiment analysis could ask the LLM to classify a review as “positive” or “negative”, or the prompt could ask the LLM “Is this review positive?” to elicit a “yes” or “no” response. <cite class="ltx_cite ltx_citemacro_citet">Zhao et al. (<a href="#bib.bib348" title="" class="ltx_ref">2021b</a>)</cite> show that these minor changes can alter the accuracy of GPT-3 by up to 30%. Similarly, minor perturbations on task-specific prompts that are logically equivalent, such as altering the order of choices in multiple-choice questions, can result in significant performance degradation <cite class="ltx_cite ltx_citemacro_cite">Pezeshkpour and Hruschka (<a href="#bib.bib213" title="" class="ltx_ref">2023</a>); Zheng et al. (<a href="#bib.bib349" title="" class="ltx_ref">2023a</a>)</cite>. 
<br class="ltx_break"></p>
</div>
</section>
<section id="Ch5.S2.SS1.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Drift</h6>

<div id="Ch5.S2.SS1.SSS0.Px3.p1" class="ltx_para">
<p id="Ch5.S2.SS1.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib30" title="" class="ltx_ref">2023b</a>)</cite> occurs when the model behind an API changes over time, so the same prompt may produce different results on the updated model. Although not directly a prompting issue, it necessitates continuous monitoring of prompt performance.</p>
</div>
</section>
</section>
<section id="Ch5.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.2.2 </span>Overconfidence and Calibration</h4>

<div id="Ch5.S2.SS2.p1" class="ltx_para">
<p id="Ch5.S2.SS2.p1.1" class="ltx_p">LLMs are often overconfident in their answers, especially when prompted to express their own confidence in words <cite class="ltx_cite ltx_citemacro_cite">Kiesler and Schiffner (<a href="#bib.bib119" title="" class="ltx_ref">2023</a>); Xiong et al. (<a href="#bib.bib315" title="" class="ltx_ref">2023a</a>)</cite>, which may lead to user overreliance on model outputs <cite class="ltx_cite ltx_citemacro_cite">Si et al. (<a href="#bib.bib265" title="" class="ltx_ref">2023c</a>)</cite>. Confidence calibration provides a score that represents the confidence of the model <cite class="ltx_cite ltx_citemacro_cite">Guo et al. (<a href="#bib.bib79" title="" class="ltx_ref">2017</a>)</cite>. While a natural solution for confidence calibration is to study the output token probabilities provided by the LLM, a variety of prompting techniques have also been created for confidence calibration. 
<br class="ltx_break"></p>
</div>
<section id="Ch5.S2.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Verbalized Score</h6>

<div id="Ch5.S2.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S2.SS2.SSS0.Px1.p1.1" class="ltx_p">is a simple calibration technique that generates a confidence score (e.g. “How confident are you from 1 to 10”), but its efficacy is under debate. <cite class="ltx_cite ltx_citemacro_citet">Xiong et al. (<a href="#bib.bib316" title="" class="ltx_ref">2023b</a>)</cite> find that several LLMs are highly overconfident when verbalizing confidence scores, even when employing self-consistency and chain-of-thought. In contrast, <cite class="ltx_cite ltx_citemacro_citet">Tian et al. (<a href="#bib.bib278" title="" class="ltx_ref">2023</a>)</cite> find that simple prompts (Section <a href="#Ch4.S2" title="4.2 Evaluation ‣ 4 Extensions of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">4.2</span></a>) can achieve more accurate calibration than the model’s output token probabilities.</p>
</div>
</section>
<section id="Ch5.S2.SS2.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Sycophancy</h6>

<div id="Ch5.S2.SS2.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S2.SS2.SSS0.Px2.p1.1" class="ltx_p">refers to the concept that LLMs will often express agreement with the user, even when that view contradicts the model’s own intial output. <cite class="ltx_cite ltx_citemacro_citet">Sharma et al. (<a href="#bib.bib256" title="" class="ltx_ref">2023</a>)</cite> find that when LLMs are asked to comment on opinions of arguments, the model is easily swayed if the user’s opinion is included in the prompt (e.g. “I really like/dislike this argument”). Further, they find that questioning the LLM’s original answer (e.g. “Are you sure?”), strongly providing an assessment of correctness (e.g. “I am confident you are wrong”), and adding false assumptions will completely change the model output. <cite class="ltx_cite ltx_citemacro_citet">Wei et al. (<a href="#bib.bib301" title="" class="ltx_ref">2023b</a>)</cite> note similar results with opinion-eliciting and false user presumptions, also finding that sycophancy is heightened for larger and instruction-tuned models. Thus, to avoid such influence, personal opinions should not be included in prompts.<span id="Ch5.footnote11" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">11</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">11</sup><span class="ltx_tag ltx_tag_note">11</span>For example, a practitioner may use the prompt template “Detect all instances where the user’s input is harmful: {INPUT}” in an attempt to prevent adversarial inputs, but this subtly makes the false presupposition that the user’s input is actually harmful. Thus, due to sycophancy, the LLM may be inclined to classify the user’s output as harmful.</span></span></span></p>
</div>
</section>
</section>
<section id="Ch5.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.2.3 </span>Biases, Stereotypes, and Culture</h4>

<div id="Ch5.S2.SS3.p1" class="ltx_para">
<p id="Ch5.S2.SS3.p1.1" class="ltx_p">LLMs should be fair to all users, such that no biases, stereotypes, or cultural harms are perpetuated in model outputs <cite class="ltx_cite ltx_citemacro_cite">Mehrabi et al. (<a href="#bib.bib178" title="" class="ltx_ref">2021</a>)</cite>. Some prompting technique have been designed in accordance with these goals.</p>
</div>
<section id="Ch5.S2.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Vanilla Prompting</h6>

<div id="Ch5.S2.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S2.SS3.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Si et al. (<a href="#bib.bib264" title="" class="ltx_ref">2023b</a>)</cite>  simply consists of an instruction in the prompt that tells the LLM to be unbiased. This technique has also been referred to as moral self-correction <cite class="ltx_cite ltx_citemacro_cite">Ganguli et al. (<a href="#bib.bib62" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="Ch5.S2.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Selecting Balanced Demonstrations</h6>

<div id="Ch5.S2.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S2.SS3.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Si et al. (<a href="#bib.bib264" title="" class="ltx_ref">2023b</a>)</cite> or obtaining demonstrations optimized over fairness metrics <cite class="ltx_cite ltx_citemacro_cite">Ma et al. (<a href="#bib.bib176" title="" class="ltx_ref">2023</a>)</cite> can reduce biases in LLM outputs (Section <a href="#Ch2.S2.SS1.SSS1" title="2.2.1.1 Few-Shot Prompting Design Decisions ‣ 2.2.1 In-Context Learning (ICL) ‣ 2.2 Text-Based Techniques ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.2.1.1</span></a>).</p>
</div>
</section>
<section id="Ch5.S2.SS3.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Cultural Awareness</h6>

<div id="Ch5.S2.SS3.SSS0.Px3.p1" class="ltx_para">
<p id="Ch5.S2.SS3.SSS0.Px3.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yao et al. (<a href="#bib.bib324" title="" class="ltx_ref">2023a</a>)</cite> can be injected into prompts to help LLMs with cultural adaptation <cite class="ltx_cite ltx_citemacro_cite">Peskov et al. (<a href="#bib.bib211" title="" class="ltx_ref">2021</a>)</cite>. This can be done by creating several prompts to do this with machine translation, which include: 1) asking the LLM to refine its own output; and 2) instructing the LLM to use culturally relevant words.</p>
</div>
</section>
<section id="Ch5.S2.SS3.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">AttrPrompt</h6>

<div id="Ch5.S2.SS3.SSS0.Px4.p1" class="ltx_para">
<p id="Ch5.S2.SS3.SSS0.Px4.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Yu et al. (<a href="#bib.bib334" title="" class="ltx_ref">2023</a>)</cite> is a prompting technique designed to avoid producing text biased towards certain attributes when generating synthetic data. Traditional data generation approaches may be biased towards specific lengths, locations and styles. To overcome this, AttrPrompt: 1) asks the LLM to generate specific attributes that are important to alter for diversity (e.g. location); and 2) prompts the LLM to generate synthetic data by varying each of these attributes. 
<br class="ltx_break"></p>
</div>
</section>
</section>
<section id="Ch5.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">5.2.4 </span>Ambiguity</h4>

<div id="Ch5.S2.SS4.p1" class="ltx_para">
<p id="Ch5.S2.SS4.p1.1" class="ltx_p">Questions that are ambiguous can be interpreted in multiple ways, where each interpretation could result in a different answer <cite class="ltx_cite ltx_citemacro_cite">Min et al. (<a href="#bib.bib185" title="" class="ltx_ref">2020</a>)</cite>. Given these multiple interpretations, ambiguous questions are challenging for existing models <cite class="ltx_cite ltx_citemacro_cite">Keyvan and Huang (<a href="#bib.bib113" title="" class="ltx_ref">2022</a>)</cite>, but a few prompting techniques have been developed to help address this challenge.</p>
</div>
<section id="Ch5.S2.SS4.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Ambiguous Demonstrations</h6>

<div id="Ch5.S2.SS4.SSS0.Px1.p1" class="ltx_para">
<p id="Ch5.S2.SS4.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_citet">Gao et al. (<a href="#bib.bib64" title="" class="ltx_ref">2023a</a>)</cite> are examples that have an ambiguous label set. Including them in a prompt can increase ICL performance. This can be automated with a retriever, but it can also be done manually. 
<br class="ltx_break"></p>
</div>
</section>
<section id="Ch5.S2.SS4.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Question Clarification</h6>

<div id="Ch5.S2.SS4.SSS0.Px2.p1" class="ltx_para">
<p id="Ch5.S2.SS4.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Rao and Daumé III (<a href="#bib.bib230" title="" class="ltx_ref">2019</a>)</cite>  allows the LLM to identify ambiguous questions and generate clarifying questions to pose to the user. Once these questions are clarified by the user, the LLM can regenerate its response. <cite class="ltx_cite ltx_citemacro_citet">Mu et al. (<a href="#bib.bib188" title="" class="ltx_ref">2023</a>)</cite> do this for code generation and <cite class="ltx_cite ltx_citemacro_citet">Zhang and Choi (<a href="#bib.bib337" title="" class="ltx_ref">2023</a>)</cite> equip LLMs with a similar pipeline for resolving ambiguity for general tasks, but explicitly design separate prompts to: 1) generate an initial answer 2) classify whether to generate clarification questions or return the initial answer 3) decide what clarification questions to generate 4) generate a final answer.</p>
</div>
</section>
</section>
</section>
</section>
<section id="Ch6" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">6   Benchmarking</h2>

<div id="Ch6.p1" class="ltx_para ltx_noindent">
<p id="Ch6.p1.1" class="ltx_p">Now that we have carried out a systematic review of prompting techniques, we will analyze the empirical performance of different techniques in two ways: via a formal benchmark evaluation, and by illustrating in detail the process of prompt engineering on a challenging real-world problem.</p>
</div>
<section id="Ch6.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">6.1 </span>Technique Benchmarking</h3>

<div id="Ch6.S1.p1" class="ltx_para">
<p id="Ch6.S1.p1.1" class="ltx_p">A formal evaluation of prompting technique might be done in a broad study that compares hundreds of them across hundreds of models and benchmarks. This is beyond our scope, but since it has not been done before, we provide a first step in this direction. We choose a subset of prompting techniques and run them on the widely used benchmark MMLU <cite class="ltx_cite ltx_citemacro_cite">Hendrycks et al. (<a href="#bib.bib89" title="" class="ltx_ref">2021</a>)</cite>. We ran on a representative subset of 2,800 MMLU questions (20% of the questions from each category).<span id="Ch6.footnote12" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">12</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">12</sup><span class="ltx_tag ltx_tag_note">12</span>We excluded human_sexuality, since gpt-3.5-turbo refused to answer these questions.</span></span></span> and used gpt-3.5-turbo for all experiments.</p>
</div>
<section id="Ch6.S1.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.1.1 </span>Comparing Prompting Techniques</h4>

<div id="Ch6.S1.SS1.p1" class="ltx_para">
<p id="Ch6.S1.SS1.p1.1" class="ltx_p">We benchmark six distinct prompting techniques using the same general prompt template (Figure <a href="#Ch6.F2" title="Figure 6.2 ‣ 6.1.2 Question Formats ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.2</span></a>). This template shows the location of different components of the prompts. Only base instructions and question exist in every prompt. The base instruction is a phrase like "Solve the problem and return (A), (B), (C) or (D)." that we vary in some cases. We additionally test two formats of the question (Figures <a href="#Ch6.F3" title="Figure 6.3 ‣ 6.1.2 Question Formats ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.3</span></a> and <a href="#Ch6.F4" title="Figure 6.4 ‣ 6.1.2 Question Formats ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.4</span></a>). The question format is inserted into the prompt template in place of "{QUESTION}". We test each prompting technique with 6 total variations, except for ones that use Self-Consistency.</p>
</div>
<section id="Ch6.S1.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Zero-Shot</h6>

<div id="Ch6.S1.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="Ch6.S1.SS1.SSS0.Px1.p1.1" class="ltx_p">As a baseline, we ran questions directly through the model without any prompting techniques. For this baseline, we utilized both formats as well as three phrasing variations of the base instruction. Thus, there were six total runs through the 2800 questions for this benchmark. This did not include any exemplars or thought inducers.</p>
</div>
</section>
<section id="Ch6.S1.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Zero-Shot-CoT Techniques</h6>

<div id="Ch6.S1.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="Ch6.S1.SS1.SSS0.Px2.p1.1" class="ltx_p">We ran also ran Zero-Shot-CoT. As the three different variations, we used three thought inducers (instructions that cause the model to generate reasoning steps) including the standard "Let’s think step by step" chain-of-thought <cite class="ltx_cite ltx_citemacro_cite">Kojima et al. (<a href="#bib.bib127" title="" class="ltx_ref">2022</a>)</cite>, as well as ThoT <cite class="ltx_cite ltx_citemacro_cite">Zhou et al. (<a href="#bib.bib355" title="" class="ltx_ref">2023</a>)</cite> and Plan and Solve <cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib290" title="" class="ltx_ref">2023f</a>)</cite>. Then, we selected the best of these, then ran it with Self-Consistency with three iterations, taking the majority response.</p>
</div>
</section>
<section id="Ch6.S1.SS1.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Few-Shot Techniques</h6>

<div id="Ch6.S1.SS1.SSS0.Px3.p1" class="ltx_para">
<p id="Ch6.S1.SS1.SSS0.Px3.p1.1" class="ltx_p">We also ran Few-Shot prompts and Few-Shot-CoT prompts, both with exemplars generated by one of our authors. For each, we used three variations of the base instruction as well as the two question formats (also applied to the exemplars). Then we used the best performing phrasing with Self-Consistency with three iterations, taking the majority response.</p>
</div>
<figure id="Ch6.F1" class="ltx_figure"><img src="/html/2406.06608/assets/x9.png" id="Ch6.F1.g1" class="ltx_graphics ltx_centering ltx_img_landscape" width="461" height="275" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.1: </span>Accuracy values are shown for each prompting technique. Purple error bars illustrate the minimum and maximum for each technique, since they were each run on different phrasings and formats (except SC).</figcaption>
</figure>
</section>
</section>
<section id="Ch6.S1.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.1.2 </span>Question Formats</h4>

<div id="Ch6.S1.SS2.p1" class="ltx_para">
<p id="Ch6.S1.SS2.p1.1" class="ltx_p">We experiment with two formatting choices from <cite class="ltx_cite ltx_citemacro_citet">Sclar et al. (<a href="#bib.bib252" title="" class="ltx_ref">2023b</a>)</cite>, who explored how formatting choices can affect benchmarking results. We use two formats which lead to varied results on their task (Figures <a href="#Ch6.F3" title="Figure 6.3 ‣ 6.1.2 Question Formats ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.3</span></a> and <a href="#Ch6.F4" title="Figure 6.4 ‣ 6.1.2 Question Formats ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.4</span></a>).</p>
</div>
<figure id="Ch6.F2" class="ltx_figure"><svg id="Ch6.F2.pic1" class="ltx_picture" height="74.6" overflow="visible" version="1.1" width="600"><g transform="translate(0,74.6) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 74.6 L 600 74.6 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 72.64 L 598.03 72.64 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="47.05" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F2.pic1.1.1.1.1.1.1" class="ltx_p">{BASE_INSTRUCTION}</span>
<span id="Ch6.F2.pic1.1.1.1.1.1.2" class="ltx_p">{EXEMPLARS}</span>
<span id="Ch6.F2.pic1.1.1.1.1.1.3" class="ltx_p">{QUESTION} {THOUGHT_INDUCER}</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption"><span class="ltx_tag ltx_tag_figure">Figure 6.2: </span>Prompt template for benchmarking.</figcaption>
</figure>
<figure id="Ch6.F3" class="ltx_figure"><svg id="Ch6.F3.pic1" class="ltx_picture" height="120.04" overflow="visible" version="1.1" width="600"><g transform="translate(0,120.04) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 120.04 L 600 120.04 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 118.07 L 598.03 118.07 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="92.48" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F3.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F3.pic1.1.1.1.1.1.1" class="ltx_p">Problem</span>
<span id="Ch6.F3.pic1.1.1.1.1.1.2" class="ltx_p">{QUESTION}</span>
<span id="Ch6.F3.pic1.1.1.1.1.1.3" class="ltx_p">Options</span>
<span id="Ch6.F3.pic1.1.1.1.1.1.4" class="ltx_p">(A)::{A} (B)::{B} (C)::{C} (D)::{D}</span>
<span id="Ch6.F3.pic1.1.1.1.1.1.5" class="ltx_p">Answer</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption"><span class="ltx_tag ltx_tag_figure">Figure 6.3: </span>Question format 1.</figcaption>
</figure>
<figure id="Ch6.F4" class="ltx_figure"><svg id="Ch6.F4.pic1" class="ltx_picture" height="107.81" overflow="visible" version="1.1" width="600"><g transform="translate(0,107.81) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 107.81 L 600 107.81 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 105.85 L 598.03 105.85 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="80.25" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F4.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F4.pic1.1.1.1.1.1.1" class="ltx_p">PROBLEM::{QUESTION}, OPTIONS::</span>
<span id="Ch6.F4.pic1.1.1.1.1.1.2" class="ltx_p">(A): {A}</span>
<span id="Ch6.F4.pic1.1.1.1.1.1.3" class="ltx_p">(B): {B}</span>
<span id="Ch6.F4.pic1.1.1.1.1.1.4" class="ltx_p">(C): {C}</span>
<span id="Ch6.F4.pic1.1.1.1.1.1.5" class="ltx_p">(D): {D}, ANSWER::</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption"><span class="ltx_tag ltx_tag_figure">Figure 6.4: </span>Question format 2.</figcaption>
</figure>
</section>
<section id="Ch6.S1.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.1.3 </span>Self-Consistency</h4>

<div id="Ch6.S1.SS3.p1" class="ltx_para">
<p id="Ch6.S1.SS3.p1.1" class="ltx_p">For the two Self-Consistency results, we set temperature to 0.5, following <cite class="ltx_cite ltx_citemacro_citet">Wang et al. (<a href="#bib.bib293" title="" class="ltx_ref">2022</a>)</cite>’s guidelines. For all other prompts, a temperature of 0 was used.</p>
</div>
</section>
<section id="Ch6.S1.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.1.4 </span>Evaluating Responses</h4>

<div id="Ch6.S1.SS4.p1" class="ltx_para">
<p id="Ch6.S1.SS4.p1.1" class="ltx_p">Evaluating whether a LLM has properly responded to a question is a difficult task (Section <a href="#Ch2.S5" title="2.5 Answer Engineering ‣ 2 A Meta-Analysis of Prompting ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">2.5</span></a>). We marked answers as correct if they followed certain identifiable patterns, such as being the only capitalized letter (A-D) within parentheses or following a phrase like “The correct answer is”.</p>
</div>
</section>
<section id="Ch6.S1.SS5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.1.5 </span>Results</h4>

<div id="Ch6.S1.SS5.p1" class="ltx_para">
<p id="Ch6.S1.SS5.p1.1" class="ltx_p">Performance generally improved as techniques grew more complex (Figure <a href="#Ch6.F1" title="Figure 6.1 ‣ Few-Shot Techniques ‣ 6.1.1 Comparing Prompting Techniques ‣ 6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.1</span></a>). However, Zero-Shot-CoT dropped precipitously from Zero-Shot. Although it had a wide spread, for all variants, Zero-Shot performed better. Both cases of Self-Consistency, naturally had lower spread since they repeated a single technique, but it only improved accuracy for Zero-Shot prompts. Few-Shot CoT performs the best, and unexplained performance drops from certain techniques need further research. As prompting technique selection is akin to hyperparameter search, this it is a very difficult task <cite class="ltx_cite ltx_citemacro_cite">Khattab et al. (<a href="#bib.bib117" title="" class="ltx_ref">2023</a>)</cite>. However, we hope this small study spurs research in the direction of more performant and robust prompting techniques.</p>
</div>
</section>
</section>
<section id="Ch6.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">6.2 </span>Prompt Engineering Case Study</h3>

<div id="Ch6.S2.p1" class="ltx_para">
<p id="Ch6.S2.p1.1" class="ltx_p">Prompt engineering is emerging as an art that many people have begun to practice professionally, but the literature does not yet include detailed guidance on the process. As a first step in this direction, we present an annotated prompt engineering case study for a difficult real-world problem. This is not intended to be an empirical contribution in terms of actually solving the problem. Rather, it provides one illustration of how an experienced prompt engineer would approach a task like this, along with lessons learned.</p>
</div>
<section id="Ch6.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.2.1 </span>Problem</h4>

<div id="Ch6.S2.SS1.p1" class="ltx_para">
<p id="Ch6.S2.SS1.p1.1" class="ltx_p">Our illustrative problem involves detection of signal that is predictive of crisis-level suicide risk in text written by a potentially suicidal individual. Suicide is a severe problem worldwide, compounded, as are most mental health issues, by a desperate lack of mental health resources. In the United States, more than half the national population lives in federally defined mental heath provider shortage areas <cite class="ltx_cite ltx_citemacro_cite">National Center for Health Workforce Analysis (<a href="#bib.bib192" title="" class="ltx_ref">2023</a>)</cite>; in addition, many mental health professionals lack core competencies in suicide prevention <cite class="ltx_cite ltx_citemacro_cite">Cramer et al. (<a href="#bib.bib40" title="" class="ltx_ref">2023</a>)</cite>. In 2021, 12.3M Americans thought seriously about suicide, with 1.7M actually making attempts resulting in over 48,000 deaths <cite class="ltx_cite ltx_citemacro_cite">CDC (<a href="#bib.bib25" title="" class="ltx_ref">2023</a>)</cite>. In the U.S., suicide was the second leading cause of death (after accidents) in people aged 10-14, 15-24, or 25-34 as of 2021 statistics, and it was the fifth leading cause of death in people aged 35–54 <cite class="ltx_cite ltx_citemacro_cite">Garnett and Curtin (<a href="#bib.bib70" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
<div id="Ch6.S2.SS1.p2" class="ltx_para">
<p id="Ch6.S2.SS1.p2.1" class="ltx_p">Recent research suggests that there is significant value in assessments of potential suicidality that focus specifically on the identification of <em id="Ch6.S2.SS1.p2.1.1" class="ltx_emph ltx_font_italic">suicidal crisis</em>, i.e. the state of acute distress associated with a high risk of imminent suicidal behavior. However, validated assessments for diagnostic approaches such as Suicide Crisis Syndrome (SCS) <cite class="ltx_cite ltx_citemacro_cite">Schuck et al. (<a href="#bib.bib247" title="" class="ltx_ref">2019b</a>); Melzer et al. (<a href="#bib.bib179" title="" class="ltx_ref">2024</a>)</cite> and Acute Suicidal Affective Disturbance <cite class="ltx_cite ltx_citemacro_citep">(Rogers et al., <a href="#bib.bib234" title="" class="ltx_ref">2019</a>)</cite> require either personal clinical interactions or completion of self-report questionnaires that contain dozens of questions. The ability to accurately flag indicators of suicidal crisis in individuals’ language could therefore have a large impact within the mental health ecosystem, not as a replacement for clinical judgment but as a way to complement existing practices <cite class="ltx_cite ltx_citemacro_citep">(Resnik et al., <a href="#bib.bib232" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
<div id="Ch6.S2.SS1.p3" class="ltx_para">
<p id="Ch6.S2.SS1.p3.1" class="ltx_p">As a starting point, we focus here on the most important predictive factor in Suicide Crisis Syndrome assessments, referred to in the literature as either <em id="Ch6.S2.SS1.p3.1.1" class="ltx_emph ltx_font_italic">frantic hopelessness</em> or <em id="Ch6.S2.SS1.p3.1.2" class="ltx_emph ltx_font_italic">entrapment</em>, “a desire to escape from an unbearable situation, tied with the perception that all escape routes are blocked” <cite class="ltx_cite ltx_citemacro_citep">(Melzer et al., <a href="#bib.bib179" title="" class="ltx_ref">2024</a>)</cite>.<span id="Ch6.footnote13" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">13</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">13</sup><span class="ltx_tag ltx_tag_note">13</span>The former term more explicitly emphasizes the frantic and desperate action required to escape an unbearable life situation. However, the term <em id="Ch6.footnote13.1" class="ltx_emph ltx_font_italic">entrapment</em> is briefer and used widely so we adopt it here.</span></span></span> This characteristic of what an individual is experiencing is also central in other characterizations of mental processes that result in suicide.</p>
</div>
</section>
<section id="Ch6.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.2.2 </span>The Dataset</h4>

<div id="Ch6.S2.SS2.p1" class="ltx_para">
<p id="Ch6.S2.SS2.p1.1" class="ltx_p">We worked with a subset of data from the University of Maryland Reddit Suicidality Dataset <cite class="ltx_cite ltx_citemacro_cite">Shing et al. (<a href="#bib.bib261" title="" class="ltx_ref">2018</a>)</cite>, which is constructed from posts in <span id="Ch6.S2.SS2.p1.1.1" class="ltx_text">r/SuicideWatch</span>, a subreddit that offers peer support for anyone struggling with suicidal thoughts. Two coders trained on the recognition of the factors in Suicide Crisis Syndrome coded a set of 221 posts for presence or absence of entrapment, achieving solid inter-coder reliability (Krippendorff’s alpha <math id="Ch6.S2.SS2.p1.1.m1.1" class="ltx_Math" alttext="=0.72" display="inline"><semantics id="Ch6.S2.SS2.p1.1.m1.1a"><mrow id="Ch6.S2.SS2.p1.1.m1.1.1" xref="Ch6.S2.SS2.p1.1.m1.1.1.cmml"><mi id="Ch6.S2.SS2.p1.1.m1.1.1.2" xref="Ch6.S2.SS2.p1.1.m1.1.1.2.cmml"></mi><mo id="Ch6.S2.SS2.p1.1.m1.1.1.1" xref="Ch6.S2.SS2.p1.1.m1.1.1.1.cmml">=</mo><mn id="Ch6.S2.SS2.p1.1.m1.1.1.3" xref="Ch6.S2.SS2.p1.1.m1.1.1.3.cmml">0.72</mn></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS2.p1.1.m1.1b"><apply id="Ch6.S2.SS2.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS2.p1.1.m1.1.1"><eq id="Ch6.S2.SS2.p1.1.m1.1.1.1.cmml" xref="Ch6.S2.SS2.p1.1.m1.1.1.1"></eq><csymbol cd="latexml" id="Ch6.S2.SS2.p1.1.m1.1.1.2.cmml" xref="Ch6.S2.SS2.p1.1.m1.1.1.2">absent</csymbol><cn type="float" id="Ch6.S2.SS2.p1.1.m1.1.1.3.cmml" xref="Ch6.S2.SS2.p1.1.m1.1.1.3">0.72</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS2.p1.1.m1.1c">=0.72</annotation></semantics></math>).</p>
</div>
</section>
<section id="Ch6.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.2.3 </span>The Process</h4>

<div id="Ch6.S2.SS3.p1" class="ltx_para">
<p id="Ch6.S2.SS3.p1.1" class="ltx_p">An expert prompt engineer, who has authored a widely used guide on prompting <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>, took on the task of using an LLM to identify entrapment in posts.<span id="Ch6.footnote14" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">14</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">14</sup><span class="ltx_tag ltx_tag_note">14</span>Disclosure: that expert is also the lead author of this paper.</span></span></span> The prompt engineer was given a brief verbal and written summary of Suicide Crisis Syndrome and entrapment, along with 121 development posts and their positive/negative labels (where “positive” means entrapment is present), the other 100 labeled posts being reserved for testing. This limited information mirrors frequent real-life scenarios in which prompts are developed based on a task description and the data. More generally, it is consistent with a tendency in natural language processing and AI more generally to approach coding (annotation) as a labeling task without delving very deeply into the fact that the labels may, in fact, refer to nuanced and complex underlying social science constructs.</p>
</div>
<div id="Ch6.S2.SS3.p2" class="ltx_para">
<p id="Ch6.S2.SS3.p2.1" class="ltx_p">We documented the prompt engineering process in order to illustrate the way that an experienced prompt engineer goes about their work. The exercise proceeded through 47 recorded development steps, cumulatively about 20 hours of work. From a cold start with 0% performance (the prompt wouldn’t return properly structured responses), performance was boosted to an F1 of 0.53, where that F1 is the harmonic mean of 0.86 precision and 0.38 recall.<span id="Ch6.footnote15" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">15</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">15</sup><span class="ltx_tag ltx_tag_note">15</span>Precision is also known as positive predictive value, and recall is also known as true positive rate or sensitivity. Although F1 is often used in computional system evaluations as a single figure of merit, we note that in this problem space its even weighting of precision and recall is probably not appropriate. We discuss this further below.</span></span></span></p>
</div>
<div id="Ch6.S2.SS3.p3" class="ltx_para">
<p id="Ch6.S2.SS3.p3.4" class="ltx_p">Below, the set of prompts <math id="Ch6.S2.SS3.p3.1.m1.1" class="ltx_Math" alttext="q_{inf}" display="inline"><semantics id="Ch6.S2.SS3.p3.1.m1.1a"><msub id="Ch6.S2.SS3.p3.1.m1.1.1" xref="Ch6.S2.SS3.p3.1.m1.1.1.cmml"><mi id="Ch6.S2.SS3.p3.1.m1.1.1.2" xref="Ch6.S2.SS3.p3.1.m1.1.1.2.cmml">q</mi><mrow id="Ch6.S2.SS3.p3.1.m1.1.1.3" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.cmml"><mi id="Ch6.S2.SS3.p3.1.m1.1.1.3.2" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.2.cmml">i</mi><mo lspace="0em" rspace="0em" id="Ch6.S2.SS3.p3.1.m1.1.1.3.1" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.S2.SS3.p3.1.m1.1.1.3.3" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.3.cmml">n</mi><mo lspace="0em" rspace="0em" id="Ch6.S2.SS3.p3.1.m1.1.1.3.1a" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.S2.SS3.p3.1.m1.1.1.3.4" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.4.cmml">f</mi></mrow></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.p3.1.m1.1b"><apply id="Ch6.S2.SS3.p3.1.m1.1.1.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.p3.1.m1.1.1.1.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.p3.1.m1.1.1.2.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.2">𝑞</ci><apply id="Ch6.S2.SS3.p3.1.m1.1.1.3.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.3"><times id="Ch6.S2.SS3.p3.1.m1.1.1.3.1.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.1"></times><ci id="Ch6.S2.SS3.p3.1.m1.1.1.3.2.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.2">𝑖</ci><ci id="Ch6.S2.SS3.p3.1.m1.1.1.3.3.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.3">𝑛</ci><ci id="Ch6.S2.SS3.p3.1.m1.1.1.3.4.cmml" xref="Ch6.S2.SS3.p3.1.m1.1.1.3.4">𝑓</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.p3.1.m1.1c">q_{inf}</annotation></semantics></math> is the test item, while <math id="Ch6.S2.SS3.p3.2.m2.1" class="ltx_Math" alttext="q_{i}" display="inline"><semantics id="Ch6.S2.SS3.p3.2.m2.1a"><msub id="Ch6.S2.SS3.p3.2.m2.1.1" xref="Ch6.S2.SS3.p3.2.m2.1.1.cmml"><mi id="Ch6.S2.SS3.p3.2.m2.1.1.2" xref="Ch6.S2.SS3.p3.2.m2.1.1.2.cmml">q</mi><mi id="Ch6.S2.SS3.p3.2.m2.1.1.3" xref="Ch6.S2.SS3.p3.2.m2.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.p3.2.m2.1b"><apply id="Ch6.S2.SS3.p3.2.m2.1.1.cmml" xref="Ch6.S2.SS3.p3.2.m2.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.p3.2.m2.1.1.1.cmml" xref="Ch6.S2.SS3.p3.2.m2.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.p3.2.m2.1.1.2.cmml" xref="Ch6.S2.SS3.p3.2.m2.1.1.2">𝑞</ci><ci id="Ch6.S2.SS3.p3.2.m2.1.1.3.cmml" xref="Ch6.S2.SS3.p3.2.m2.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.p3.2.m2.1c">q_{i}</annotation></semantics></math>, <math id="Ch6.S2.SS3.p3.3.m3.1" class="ltx_Math" alttext="r_{i}" display="inline"><semantics id="Ch6.S2.SS3.p3.3.m3.1a"><msub id="Ch6.S2.SS3.p3.3.m3.1.1" xref="Ch6.S2.SS3.p3.3.m3.1.1.cmml"><mi id="Ch6.S2.SS3.p3.3.m3.1.1.2" xref="Ch6.S2.SS3.p3.3.m3.1.1.2.cmml">r</mi><mi id="Ch6.S2.SS3.p3.3.m3.1.1.3" xref="Ch6.S2.SS3.p3.3.m3.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.p3.3.m3.1b"><apply id="Ch6.S2.SS3.p3.3.m3.1.1.cmml" xref="Ch6.S2.SS3.p3.3.m3.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.p3.3.m3.1.1.1.cmml" xref="Ch6.S2.SS3.p3.3.m3.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.p3.3.m3.1.1.2.cmml" xref="Ch6.S2.SS3.p3.3.m3.1.1.2">𝑟</ci><ci id="Ch6.S2.SS3.p3.3.m3.1.1.3.cmml" xref="Ch6.S2.SS3.p3.3.m3.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.p3.3.m3.1c">r_{i}</annotation></semantics></math>, and <math id="Ch6.S2.SS3.p3.4.m4.1" class="ltx_Math" alttext="a_{i}" display="inline"><semantics id="Ch6.S2.SS3.p3.4.m4.1a"><msub id="Ch6.S2.SS3.p3.4.m4.1.1" xref="Ch6.S2.SS3.p3.4.m4.1.1.cmml"><mi id="Ch6.S2.SS3.p3.4.m4.1.1.2" xref="Ch6.S2.SS3.p3.4.m4.1.1.2.cmml">a</mi><mi id="Ch6.S2.SS3.p3.4.m4.1.1.3" xref="Ch6.S2.SS3.p3.4.m4.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.p3.4.m4.1b"><apply id="Ch6.S2.SS3.p3.4.m4.1.1.cmml" xref="Ch6.S2.SS3.p3.4.m4.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.p3.4.m4.1.1.1.cmml" xref="Ch6.S2.SS3.p3.4.m4.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.p3.4.m4.1.1.2.cmml" xref="Ch6.S2.SS3.p3.4.m4.1.1.2">𝑎</ci><ci id="Ch6.S2.SS3.p3.4.m4.1.1.3.cmml" xref="Ch6.S2.SS3.p3.4.m4.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.p3.4.m4.1c">a_{i}</annotation></semantics></math> denote the questions, chain-of-thought steps, and answers in exemplars.</p>
</div>
<figure id="Ch6.F5" class="ltx_figure"><img src="/html/2406.06608/assets/x10.png" id="Ch6.F5.g1" class="ltx_graphics ltx_centering ltx_img_landscape" width="461" height="367" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.5: </span>F1 scores varied widely from worst performing prompts to highest performing prompts, but most prompts scored within a similar range.</figcaption>
</figure>
<figure id="Ch6.F6" class="ltx_figure"><img src="/html/2406.06608/assets/x11.png" id="Ch6.F6.g1" class="ltx_graphics ltx_centering ltx_img_square" width="461" height="418" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.6: </span>From the first prompt tried (Zero-Shot + Context) to the last (Anonymized Email), improvements in F1 score were hard to come by and and often involved testing multiple underperforming prompts before finding a performant one. Green lines show improvements over the current highest F1 score, while red lines show deteriorations.</figcaption>
</figure>
<section id="Ch6.S2.SS3.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">6.2.3.1 </span>Dataset Exploration (2 steps)</h5>

<div id="Ch6.S2.SS3.SSS1.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS1.p1.1" class="ltx_p">The process began with the prompt engineer reviewing a description of entrapment (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>); this description had been used as a first-pass rubric for the human coders early in the coding process, noting, however, that they were familiar with SCS and knew it was neither a formal definition nor exhaustive. The prompt engineer then loaded the dataset into a Python notebook for data exploration purposes. He began by asking gpt-4-turbo-preview if it knew what entrapment was (Figure <a href="#Ch6.F8" title="Figure 6.8 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.8</span></a>), but found that the LLM’s response was not similar to the description that had been given. In consequence, the prompt engineer included the Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a> description of entrapment in all future prompts.</p>
</div>
<figure id="Ch6.F7" class="ltx_figure"><svg id="Ch6.F7.pic1" class="ltx_picture ltx_centering" height="205.9" overflow="visible" version="1.1" width="600"><g transform="translate(0,205.9) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 205.9 L 600 205.9 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 203.93 L 598.03 203.93 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="178.34" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F7.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F7.pic1.1.1.1.1.1.1" class="ltx_p">Entrapment:</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.2" class="ltx_p">- Feeling like there is no exit</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.3" class="ltx_p">- Feeling hopeless</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.4" class="ltx_p">- Feeling like there is no way out</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.5" class="ltx_p">- Feeling afraid that things will never be normal again</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.6" class="ltx_p">- Feeling helpless to change</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.7" class="ltx_p">- Feeling trapped</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.8" class="ltx_p">- Feeling doomed</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.9" class="ltx_p">- Feeling or thinking that things will never change</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.10" class="ltx_p">- Feeling like there is no escape</span>
<span id="Ch6.F7.pic1.1.1.1.1.1.11" class="ltx_p">- Feeling like there are no good solutions to problems</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.7: </span>The description of entrapment used by the prompt engineer</figcaption>
</figure>
<figure id="Ch6.F8" class="ltx_figure"><svg id="Ch6.F8.pic1" class="ltx_picture ltx_centering" height="39.86" overflow="visible" version="1.1" width="600"><g transform="translate(0,39.86) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 39.86 L 600 39.86 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 37.89 L 598.03 37.89 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="12.3" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F8.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F8.pic1.1.1.1.1.1.1" class="ltx_p">What is entrapment with respect to Suicide Crisis Syndrome?</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.8: </span>Question asked to the LLM to determine whether its training data had provided relevant knowledge about entrapment (it had not).</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">6.2.3.2 </span>Getting a Label (8 steps)</h5>

<div id="Ch6.S2.SS3.SSS2.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS2.p1.1" class="ltx_p">As noted in Section <a href="#Ch6.S1" title="6.1 Technique Benchmarking ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.1</span></a> with regard to the human_sexuality subset of MMLU, LLMs exhibit unpredictable and difficult to control behaviour in sensitive domains. For multiple steps in the prompt engineering process, the prompt engineer found that the LLM was giving mental health advice (e.g. Figure <a href="#Ch6.F9" title="Figure 6.9 ‣ 6.2.3.2 Getting a Label (8 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.9</span></a>) instead of labeling the input. This was addressed by switching to the GPT-4-32K model.</p>
</div>
<div id="Ch6.S2.SS3.SSS2.p2" class="ltx_para">
<p id="Ch6.S2.SS3.SSS2.p2.1" class="ltx_p">A take-away from this initial phase is that the “guard rails” associated with some large language models may interfere with the ability to make progress on a prompting task, and this could influence the choice of model for reasons other than the LLM’s potential quality.</p>
</div>
<figure id="Ch6.F9" class="ltx_figure"><svg id="Ch6.F9.pic1" class="ltx_picture ltx_centering" height="56.46" overflow="visible" version="1.1" width="600"><g transform="translate(0,56.46) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 56.46 L 600 56.46 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 54.49 L 598.03 54.49 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="28.9" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F9.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F9.pic1.1.1.1.1.1.1" class="ltx_p">If you’re in immediate danger of harming yourself, please contact emergency services or a crisis hotline in your area. They can provide immediate support and help ensure your safety.</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.9: </span>A snippet from an output, which does not label the data point, but rather attempts to provide mental health support to the user. Such outputs are often five times as long as this snippet.</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">6.2.3.3 </span>Prompting Techniques (32 steps)</h5>

<div id="Ch6.S2.SS3.SSS3.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.p1.1" class="ltx_p">The prompt engineer then spent the majority of his time improving the prompting technique being used. This included techniques such as Few-Shot, Chain-of-Thought, AutoCoT, Contrastive CoT, and multiple answer extraction techniques. We report statistics for the first runs of these techniques; F1 scores could change by as much as 0.04 upon subsequent runs, even with temperature and top p set to zero.<span id="Ch6.footnote16" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">16</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">16</sup><span class="ltx_tag ltx_tag_note">16</span>Temperature and top-p are configuration hyperparameters that control randomness of the output <cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>.</span></span></span></p>
</div>
<section id="Ch6.S2.SS3.SSS3.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Zero-Shot + Context</h6>

<div id="Ch6.S2.SS3.SSS3.Px1.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px1.p1.1" class="ltx_p">was the first technique evaluated (Figure <a href="#Ch6.F10" title="Figure 6.10 ‣ Zero-Shot + Context ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.10</span></a>), using the description in Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>. Notice the word <em id="Ch6.S2.SS3.SSS3.Px1.p1.1.1" class="ltx_emph ltx_font_italic">definition</em> in the prompt, although Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a> is not a formal definition.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px1.p2" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px1.p2.1" class="ltx_p">In order to obtain a final response from the LLM to use in calculating performance metrics, it was necessary to extract a label from the LLM output. The prompt engineer tested two extractors, one that checks if the output is exactly "Yes" or "No", and another which just checks if those words match the first few characters of the output. The latter had better performance, and it is used for the rest of this section until we reach CoT. This approach obtained a 0.25 recall, 1.0 precision, and 0.40 F1, evaluated on all samples from the training/development since no samples had been used as exemplars.</p>
</div>
<figure id="Ch6.F10" class="ltx_figure"><svg id="Ch6.F10.pic1" class="ltx_picture ltx_centering" height="73.07" overflow="visible" version="1.1" width="600"><g transform="translate(0,73.07) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 73.07 L 600 73.07 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 71.1 L 598.03 71.1 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="45.51" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.2" class="ltx_p">{ENTRAPMENT DEFINITION (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>)}</span>
<span id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_p"><math id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="\{q_{inf}\}" display="inline"><semantics id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1a"><mrow id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml"><mo stretchy="false" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.2" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml">{</mo><msub id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.cmml"><mi id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.2" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.2.cmml">q</mi><mrow id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.cmml"><mi id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.2" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.2.cmml">i</mi><mo lspace="0em" rspace="0em" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1.cmml">​</mo><mi id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.3" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.3.cmml">n</mi><mo lspace="0em" rspace="0em" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1a" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1.cmml">​</mo><mi id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.4" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.4.cmml">f</mi></mrow></msub><mo stretchy="false" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.3" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml">}</mo></mrow><annotation-xml encoding="MathML-Content" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1b"><set id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1"><apply id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.1.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1">subscript</csymbol><ci id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.2.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.2">𝑞</ci><apply id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3"><times id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.1"></times><ci id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.2.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.2">𝑖</ci><ci id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.3.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.3">𝑛</ci><ci id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.4.cmml" xref="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.1.3.4">𝑓</ci></apply></apply></set></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1c">\{q_{inf}\}</annotation></semantics></math></span>
<span id="Ch6.F10.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.3" class="ltx_p">Is this entrapment? Yes or no.</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.10: </span>A Zero-Shot + Context prompt, the simplest of all prompts explored in this case study.</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot + Context.</h6>

<div id="Ch6.S2.SS3.SSS3.Px2.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px2.p1.3" class="ltx_p">Next, the prompt engineer added the first ten data samples (with labels) into the prompt, in Q: (question) A: (answer) format (Figure <a href="#Ch6.F11" title="Figure 6.11 ‣ 10-Shot + Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.11</span></a>). This 10-shot prompt was evaluated on the remaining items in the training/development set, yielding <math id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px2.p1.1.m1.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px2.p1.3.1" class="ltx_text" style="color:#00FF00;"> 0.05</span> (0.30) recall, <math id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px2.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px2.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.70</span> (0.30) precision, and <math id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px2.p1.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px2.p1.3.3" class="ltx_text" style="color:#00FF00;">0.05</span> (0.45) F1 relative to the previous best prompt.<span id="Ch6.footnote17" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">17</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">17</sup><span class="ltx_tag ltx_tag_note">17</span>Here and for the remainder of the case study, we judge “best” by F1, and we report on the current prompt under discussion relative to the best performing previous prompt.</span></span></span></p>
</div>
<figure id="Ch6.F11" class="ltx_figure"><svg id="Ch6.F11.pic1" class="ltx_picture ltx_centering" height="153.24" overflow="visible" version="1.1" width="600"><g transform="translate(0,153.24) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 153.24 L 600 153.24 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 151.28 L 598.03 151.28 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="125.69" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F11.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F11.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.6" class="ltx_p">{ENTRAPMENT DEFINITION (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>)}</span>
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<span id="Ch6.F11.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.7" class="ltx_p">…</span>
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<span id="Ch6.F11.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.8" class="ltx_p">A:</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.11: </span>10-Shot + Context Prompt</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">One-Shot AutoDiCot + Full Context.</h6>

<div id="Ch6.S2.SS3.SSS3.Px3.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p1.1" class="ltx_p">After performing 10-shot prompting, the prompt engineer observed that the 12th item in the development set was being incorrectly being labeled as a positive instance, and began experimenting with ways of modifying the prompting such that the model would get that item correct. In order to get a sense of why this mislabeling was taking place, the prompt engineer prompted the LLM to generate an explanation of why the 12th item would have been labeled the way it was.<span id="Ch6.footnote18" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">18</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">18</sup><span class="ltx_tag ltx_tag_note">18</span>We are trying to avoid misleading language like “the LLM generated an explanation of its reasoning”. LLMs do not have access to their own internal processes, and therefore they cannot “explain their reasoning” in the usual sense. An LLM generating an “explanation” is producing description of potential reasoning steps in getting to the output that could be true, but also may not be accurate at all.</span></span></span></p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px3.p2" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p2.5" class="ltx_p">Figure <a href="#Ch6.F12" title="Figure 6.12 ‣ One-Shot AutoDiCot + Full Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.12</span></a> shows a version of that process, generalized to produce explanations for all development question/answer items (<math id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2" class="ltx_Math" alttext="q_{i},a_{i}" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2a"><mrow id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.3.cmml"><msub id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.2.cmml">q</mi><mi id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.3.cmml">i</mi></msub><mo id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.3.cmml">,</mo><msub id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.cmml"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.2.cmml">a</mi><mi id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.3.cmml">i</mi></msub></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2b"><list id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2"><apply id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.2">𝑞</ci><ci id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.1.1.1.1.3">𝑖</ci></apply><apply id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2"><csymbol cd="ambiguous" id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2">subscript</csymbol><ci id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.2">𝑎</ci><ci id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2.2.2.2.3">𝑖</ci></apply></list></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p2.1.m1.2c">q_{i},a_{i}</annotation></semantics></math>) in a set <math id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1" class="ltx_Math" alttext="T" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1a"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1.1.cmml">T</mi><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1.1">𝑇</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p2.2.m2.1c">T</annotation></semantics></math> rather than just item 12. Informed by the reasoning steps <math id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1" class="ltx_Math" alttext="r_{12}" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1a"><msub id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.2.cmml">r</mi><mn id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.3.cmml">12</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1b"><apply id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.2">𝑟</ci><cn type="integer" id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1.1.3">12</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p2.3.m3.1c">r_{12}</annotation></semantics></math> elicited with respect to the incorrectly labeled <math id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1" class="ltx_Math" alttext="q_{12}" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1a"><msub id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.2.cmml">q</mi><mn id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.3.cmml">12</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1b"><apply id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.2">𝑞</ci><cn type="integer" id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1.1.3">12</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p2.4.m4.1c">q_{12}</annotation></semantics></math>, the previous prompt was modified by including <math id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1" class="ltx_Math" alttext="r_{12}" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1a"><msub id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.2" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.2.cmml">r</mi><mn id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.3" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.3.cmml">12</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1b"><apply id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1"><csymbol cd="ambiguous" id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1">subscript</csymbol><ci id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.2">𝑟</ci><cn type="integer" id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1.1.3">12</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p2.5.m5.1c">r_{12}</annotation></semantics></math> in a One-Shot CoT example with <em id="Ch6.S2.SS3.SSS3.Px3.p2.5.1" class="ltx_emph ltx_font_italic">incorrect</em> reasoning, as an exemplar for what <em id="Ch6.S2.SS3.SSS3.Px3.p2.5.2" class="ltx_emph ltx_font_italic">not</em> to do (Figure <a href="#Ch6.F13" title="Figure 6.13 ‣ One-Shot AutoDiCot + Full Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.13</span></a>).</p>
</div>
<figure id="Ch6.F12" class="ltx_figure"><svg id="Ch6.F12.pic1" class="ltx_picture ltx_centering" height="105.58" overflow="visible" version="1.1" width="600"><g transform="translate(0,105.58) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 5.91 L 0 99.68 C 0 102.94 2.64 105.58 5.91 105.58 L 594.09 105.58 C 597.36 105.58 600 102.94 600 99.68 L 600 5.91 C 600 2.64 597.36 0 594.09 0 L 5.91 0 C 2.64 0 0 2.64 0 5.91 Z" style="stroke:none"></path></g><g fill="#F2F2F2" fill-opacity="1.0"><path d="M 1.97 5.91 L 1.97 99.68 C 1.97 101.85 3.73 103.62 5.91 103.62 L 594.09 103.62 C 596.27 103.62 598.03 101.85 598.03 99.68 L 598.03 5.91 C 598.03 3.73 596.27 1.97 594.09 1.97 L 5.91 1.97 C 3.73 1.97 1.97 3.73 1.97 5.91 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="78.03" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F12.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.S2.I1" class="ltx_enumerate">
<span id="Ch6.S2.I1.i1" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">1.</span> 
<span id="Ch6.S2.I1.i1.p1" class="ltx_para">
<span id="Ch6.S2.I1.i1.p1.3" class="ltx_p">Require: Development items <math id="Ch6.S2.I1.i1.p1.1.m1.1" class="ltx_Math" alttext="T" display="inline"><semantics id="Ch6.S2.I1.i1.p1.1.m1.1a"><mi id="Ch6.S2.I1.i1.p1.1.m1.1.1" xref="Ch6.S2.I1.i1.p1.1.m1.1.1.cmml">T</mi><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i1.p1.1.m1.1b"><ci id="Ch6.S2.I1.i1.p1.1.m1.1.1.cmml" xref="Ch6.S2.I1.i1.p1.1.m1.1.1">𝑇</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i1.p1.1.m1.1c">T</annotation></semantics></math> with <math id="Ch6.S2.I1.i1.p1.2.m2.1" class="ltx_Math" alttext="n" display="inline"><semantics id="Ch6.S2.I1.i1.p1.2.m2.1a"><mi id="Ch6.S2.I1.i1.p1.2.m2.1.1" xref="Ch6.S2.I1.i1.p1.2.m2.1.1.cmml">n</mi><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i1.p1.2.m2.1b"><ci id="Ch6.S2.I1.i1.p1.2.m2.1.1.cmml" xref="Ch6.S2.I1.i1.p1.2.m2.1.1">𝑛</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i1.p1.2.m2.1c">n</annotation></semantics></math> pairs <math id="Ch6.S2.I1.i1.p1.3.m3.2" class="ltx_Math" alttext="(q_{i},a_{i})" display="inline"><semantics id="Ch6.S2.I1.i1.p1.3.m3.2a"><mrow id="Ch6.S2.I1.i1.p1.3.m3.2.2.2" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.3.cmml"><mo stretchy="false" id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.3" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.3.cmml">(</mo><msub id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.cmml"><mi id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.2" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.2.cmml">q</mi><mi id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.3" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.4" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.3.cmml">,</mo><msub id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.cmml"><mi id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.2" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.2.cmml">a</mi><mi id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.3" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.3.cmml">i</mi></msub><mo stretchy="false" id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.5" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.3.cmml">)</mo></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i1.p1.3.m3.2b"><interval closure="open" id="Ch6.S2.I1.i1.p1.3.m3.2.2.3.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2"><apply id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.1.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.2.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.2">𝑞</ci><ci id="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.3.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.1.1.1.1.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2"><csymbol cd="ambiguous" id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.1.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2">subscript</csymbol><ci id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.2.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.2">𝑎</ci><ci id="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.3.cmml" xref="Ch6.S2.I1.i1.p1.3.m3.2.2.2.2.3">𝑖</ci></apply></interval></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i1.p1.3.m3.2c">(q_{i},a_{i})</annotation></semantics></math></span>
</span></span>
<span id="Ch6.S2.I1.i2" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">2.</span> 
<span id="Ch6.S2.I1.i2.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.p1.2" class="ltx_p">For each pair <math id="Ch6.S2.I1.i2.p1.1.m1.2" class="ltx_Math" alttext="(q_{i},a_{i})" display="inline"><semantics id="Ch6.S2.I1.i2.p1.1.m1.2a"><mrow id="Ch6.S2.I1.i2.p1.1.m1.2.2.2" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.3.cmml"><mo stretchy="false" id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.3" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.3.cmml">(</mo><msub id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.cmml"><mi id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.2" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.2.cmml">q</mi><mi id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.3" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.4" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.3.cmml">,</mo><msub id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.cmml"><mi id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.2" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.2.cmml">a</mi><mi id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.3" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.3.cmml">i</mi></msub><mo stretchy="false" id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.5" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.3.cmml">)</mo></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.p1.1.m1.2b"><interval closure="open" id="Ch6.S2.I1.i2.p1.1.m1.2.2.3.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2"><apply id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.1.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.2.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.2">𝑞</ci><ci id="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.3.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.1.1.1.1.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.1.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2">subscript</csymbol><ci id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.2.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.2">𝑎</ci><ci id="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.3.cmml" xref="Ch6.S2.I1.i2.p1.1.m1.2.2.2.2.3">𝑖</ci></apply></interval></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.p1.1.m1.2c">(q_{i},a_{i})</annotation></semantics></math> in <math id="Ch6.S2.I1.i2.p1.2.m2.1" class="ltx_Math" alttext="T" display="inline"><semantics id="Ch6.S2.I1.i2.p1.2.m2.1a"><mi id="Ch6.S2.I1.i2.p1.2.m2.1.1" xref="Ch6.S2.I1.i2.p1.2.m2.1.1.cmml">T</mi><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.p1.2.m2.1b"><ci id="Ch6.S2.I1.i2.p1.2.m2.1.1.cmml" xref="Ch6.S2.I1.i2.p1.2.m2.1.1">𝑇</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.p1.2.m2.1c">T</annotation></semantics></math>:</span>
<span id="Ch6.S2.I1.i2.I1" class="ltx_enumerate">
<span id="Ch6.S2.I1.i2.I1.i1" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">(a)</span> 
<span id="Ch6.S2.I1.i2.I1.i1.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i1.p1.1" class="ltx_p">Label <math id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1" class="ltx_Math" alttext="q_{i}" display="inline"><semantics id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1a"><msub id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.cmml"><mi id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.2" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.2.cmml">q</mi><mi id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.3" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1b"><apply id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.2.cmml" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.2">𝑞</ci><ci id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.3.cmml" xref="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.I1.i1.p1.1.m1.1c">q_{i}</annotation></semantics></math> as entrapment or not entrapment using the model</span>
</span></span>
<span id="Ch6.S2.I1.i2.I1.i2" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">(b)</span> 
<span id="Ch6.S2.I1.i2.I1.i2.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i2.p1.1" class="ltx_p">If the model labels correctly:</span>
<span id="Ch6.S2.I1.i2.I1.i2.I1" class="ltx_enumerate">
<span id="Ch6.S2.I1.i2.I1.i2.I1.i1" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">i.</span> 
<span id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1" class="ltx_p">Prompt the model with "Why?" to generate a reasoning chain <math id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1" class="ltx_Math" alttext="r_{i}" display="inline"><semantics id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1a"><msub id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.cmml"><mi id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.2" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.2.cmml">r</mi><mi id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.3" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1b"><apply id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.2.cmml" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.2">𝑟</ci><ci id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.3.cmml" xref="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.I1.i2.I1.i1.p1.1.m1.1c">r_{i}</annotation></semantics></math></span>
</span></span>
</span>
</span></span>
<span id="Ch6.S2.I1.i2.I1.i3" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">(c)</span> 
<span id="Ch6.S2.I1.i2.I1.i3.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i3.p1.1" class="ltx_p">Else:</span>
<span id="Ch6.S2.I1.i2.I1.i3.I1" class="ltx_enumerate">
<span id="Ch6.S2.I1.i2.I1.i3.I1.i1" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">i.</span> 
<span id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1" class="ltx_p">Prompt the model with "It is actually [is/is not] entrapment, please explain why." to generate a reasoning chain <math id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1" class="ltx_Math" alttext="r_{i}" display="inline"><semantics id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1a"><msub id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.cmml"><mi id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.2" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.2.cmml">r</mi><mi id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.3" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1b"><apply id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.2.cmml" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.2">𝑟</ci><ci id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.3.cmml" xref="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.I1.i3.I1.i1.p1.1.m1.1c">r_{i}</annotation></semantics></math></span>
</span></span>
</span>
</span></span>
<span id="Ch6.S2.I1.i2.I1.i4" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">(d)</span> 
<span id="Ch6.S2.I1.i2.I1.i4.p1" class="ltx_para">
<span id="Ch6.S2.I1.i2.I1.i4.p1.1" class="ltx_p">Store the tuple <math id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3" class="ltx_Math" alttext="(q_{i},r_{i},a_{i})" display="inline"><semantics id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3a"><mrow id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml"><mo stretchy="false" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.4" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml">(</mo><msub id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.cmml"><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.2" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.2.cmml">q</mi><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.3" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.5" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml">,</mo><msub id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.cmml"><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.2" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.2.cmml">r</mi><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.3" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.6" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml">,</mo><msub id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.cmml"><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.2" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.2.cmml">a</mi><mi id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.3" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.3.cmml">i</mi></msub><mo stretchy="false" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.7" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml">)</mo></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3b"><vector id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.4.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3"><apply id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.1.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.2.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.2">𝑞</ci><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.3.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.1.1.1.1.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.1.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.2.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.2">𝑟</ci><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.3.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.2.2.2.2.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3"><csymbol cd="ambiguous" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.1.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3">subscript</csymbol><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.2.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.2">𝑎</ci><ci id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.3.cmml" xref="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3.3.3.3.3">𝑖</ci></apply></vector></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i2.I1.i4.p1.1.m1.3c">(q_{i},r_{i},a_{i})</annotation></semantics></math></span>
</span></span>
</span>
</span></span>
<span id="Ch6.S2.I1.i3" class="ltx_item" style="list-style-type:none;"><span class="ltx_tag ltx_tag_item">3.</span> 
<span id="Ch6.S2.I1.i3.p1" class="ltx_para">
<span id="Ch6.S2.I1.i3.p1.2" class="ltx_p">Return: <math id="Ch6.S2.I1.i3.p1.1.m1.1" class="ltx_Math" alttext="n" display="inline"><semantics id="Ch6.S2.I1.i3.p1.1.m1.1a"><mi id="Ch6.S2.I1.i3.p1.1.m1.1.1" xref="Ch6.S2.I1.i3.p1.1.m1.1.1.cmml">n</mi><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i3.p1.1.m1.1b"><ci id="Ch6.S2.I1.i3.p1.1.m1.1.1.cmml" xref="Ch6.S2.I1.i3.p1.1.m1.1.1">𝑛</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i3.p1.1.m1.1c">n</annotation></semantics></math> tuples <math id="Ch6.S2.I1.i3.p1.2.m2.3" class="ltx_Math" alttext="(q_{i},r_{i},a_{i})" display="inline"><semantics id="Ch6.S2.I1.i3.p1.2.m2.3a"><mrow id="Ch6.S2.I1.i3.p1.2.m2.3.3.3" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml"><mo stretchy="false" id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.4" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml">(</mo><msub id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.cmml"><mi id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.2" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.2.cmml">q</mi><mi id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.3" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.5" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml">,</mo><msub id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.cmml"><mi id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.2" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.2.cmml">r</mi><mi id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.3" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.3.cmml">i</mi></msub><mo id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.6" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml">,</mo><msub id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.cmml"><mi id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.2" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.2.cmml">a</mi><mi id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.3" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.3.cmml">i</mi></msub><mo stretchy="false" id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.7" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml">)</mo></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.I1.i3.p1.2.m2.3b"><vector id="Ch6.S2.I1.i3.p1.2.m2.3.3.4.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3"><apply id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1"><csymbol cd="ambiguous" id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.1.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1">subscript</csymbol><ci id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.2.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.2">𝑞</ci><ci id="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.3.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.1.1.1.1.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2"><csymbol cd="ambiguous" id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.1.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2">subscript</csymbol><ci id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.2.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.2">𝑟</ci><ci id="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.3.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.2.2.2.2.3">𝑖</ci></apply><apply id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3"><csymbol cd="ambiguous" id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.1.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3">subscript</csymbol><ci id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.2.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.2">𝑎</ci><ci id="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.3.cmml" xref="Ch6.S2.I1.i3.p1.2.m2.3.3.3.3.3">𝑖</ci></apply></vector></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.I1.i3.p1.2.m2.3c">(q_{i},r_{i},a_{i})</annotation></semantics></math></span>
</span></span>
</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.12: </span>Algorithm: Automatic Directed CoT</figcaption>
</figure>
<div id="Ch6.S2.SS3.SSS3.Px3.p3" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p3.1" class="ltx_p">We call the algorithm in Figure <a href="#Ch6.F12" title="Figure 6.12 ‣ One-Shot AutoDiCot + Full Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.12</span></a> Automatic Directed CoT (AutoDiCoT), since it automatically directs the CoT process to reason in a particular way. This technique can be generalized to any labeling task. It combines the automatic generation of CoTs <cite class="ltx_cite ltx_citemacro_cite">Zhang et al. (<a href="#bib.bib342" title="" class="ltx_ref">2022b</a>)</cite> with showing the LLM examples of bad reasoning, as in the case of Contrastive CoT <cite class="ltx_cite ltx_citemacro_cite">Chia et al. (<a href="#bib.bib37" title="" class="ltx_ref">2023</a>)</cite>. The algorithm was also used in developing later prompts.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px3.p4" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p4.1" class="ltx_p">Finally, the prompt was extended with two additional pieces of context/instruction. The first was an email message the prompt engineer had received explaining overall goals of the project, which provided more context around the concept of entrapment and the reasons for wanting to label it. The second addition was inspired by the prompt engineer noticing the model was frequently over-generating a positive label for entrapment. Hypothesizing that the model was being too aggressive in its pretraining-based inferences from the overt language, he instructed the model to restrict itself to <em id="Ch6.S2.SS3.SSS3.Px3.p4.1.1" class="ltx_emph ltx_font_italic">explicit</em> statements of entrapment (Figure <a href="#Ch6.F13" title="Figure 6.13 ‣ One-Shot AutoDiCot + Full Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.13</span></a>). Below we refer to these two pieces of context, provided in addition to the description of entrapment, as <span id="Ch6.S2.SS3.SSS3.Px3.p4.1.2" class="ltx_text ltx_font_italic">full context</span>.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px3.p5" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p5.3" class="ltx_p">A new extractor was also used for this prompt, which checks if the last word in the output is "Yes" or "No", instead of the first word.
This updated prompt was tested against all inputs in the development set except for the first 20. It did not improve F1, <math id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p5.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px3.p5.3.1" class="ltx_text" style="color:#FF0000;">0.09</span> (0.36) F1, but it led the prompt engineer in a direction that did, as discussed below. Precision improved to <math id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p5.2.m2.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px3.p5.3.2" class="ltx_text" style="color:#00FF00;"> 0.09</span> (0.39) precision and recall dropped <math id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px3.p5.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px3.p5.3.3" class="ltx_text" style="color:#00FF00;"> 0.03</span> (0.33) recall.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px3.p6" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p6.1" class="ltx_p">At this point, though, it is worth observing that, although it did ultimately lead to a gain in F1 score, the steps taken here to cut down on over-generation of positive labels were not, in fact, the right move in terms of the longer term goals. Entrapment need <em id="Ch6.S2.SS3.SSS3.Px3.p6.1.1" class="ltx_emph ltx_font_italic">not</em> be expressed explicitly in order to be present (e.g. through phrases like “I feel trapped” or “There’s no way out”); rather, clinical experts who have looked at the texts found that expressions of entrapment could be implicit and potentially quite nuanced. Moreover, in most use cases for automatically spotting entrapment in someone’s language, precision and recall are unlikely to be equally important and, of the two, the recall/sensitivity (i.e. not missing people who should be flagged as at-risk) may matter more because the potential cost of a false negative is so high.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px3.p7" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px3.p7.1" class="ltx_p">The take-away here, although the insight came later, is that it is easy for the process of prompt development to diverge from the actual goals unless regular engagement is fostered between the prompt engineer and domain experts who more deeply understand the real-world use case.</p>
</div>
<figure id="Ch6.F13" class="ltx_figure"><svg id="Ch6.F13.pic1" class="ltx_picture ltx_centering" height="291.2" overflow="visible" version="1.1" width="600"><g transform="translate(0,291.2) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 291.2 L 600 291.2 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 289.23 L 598.03 289.23 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="263.64" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2" class="ltx_p"><math id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="\{" display="inline"><semantics id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1a"><mo stretchy="false" id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1" xref="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml">{</mo><annotation-xml encoding="MathML-Content" id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1b"><ci id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml" xref="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1">{</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F13.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1c">\{</annotation></semantics></math>PROFESSOR’S EMAIL<math id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1" class="ltx_Math" alttext="\}" display="inline"><semantics id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1a"><mo stretchy="false" id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1.1" xref="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1.1.cmml">}</mo><annotation-xml encoding="MathML-Content" id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1b"><ci id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1.1.cmml" xref="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1.1">}</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F13.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m2.1c">\}</annotation></semantics></math></span>
<span id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4" class="ltx_p"><math id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1" class="ltx_Math" alttext="\{" display="inline"><semantics id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1a"><mo stretchy="false" id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1" xref="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.cmml">{</mo><annotation-xml encoding="MathML-Content" id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1b"><ci id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.cmml" xref="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1">{</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F13.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1c">\{</annotation></semantics></math>ENTRAPMENT DEFINITION (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>)<math id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1" class="ltx_Math" alttext="\}" display="inline"><semantics id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1a"><mo stretchy="false" id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1.1" xref="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1.1.cmml">}</mo><annotation-xml encoding="MathML-Content" id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1b"><ci id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1.1.cmml" xref="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1.1">}</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F13.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m2.1c">\}</annotation></semantics></math></span>
<span id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.8" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
<span id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5" class="ltx_p">Q: {<math id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1" class="ltx_math_unparsed" alttext="q_{12}\}" display="inline"><semantics id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1a"><mrow id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1b"><msub id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1"><mi id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2">q</mi><mn id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3">12</mn></msub><mo stretchy="false" id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.2">}</mo></mrow><annotation encoding="application/x-tex" id="Ch6.F13.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1c">q_{12}\}</annotation></semantics></math></span>
<span id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.9" class="ltx_p">R: Although "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.
seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.</span>
<span id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6" class="ltx_p">A: {<math id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1" class="ltx_math_unparsed" alttext="a_{12}\}" display="inline"><semantics id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1a"><mrow id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1b"><msub id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1"><mi id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.2">a</mi><mn id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.3">12</mn></msub><mo stretchy="false" id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.2">}</mo></mrow><annotation encoding="application/x-tex" id="Ch6.F13.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1c">a_{12}\}</annotation></semantics></math></span>
<span id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_p">Q: {<math id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1" class="ltx_math_unparsed" alttext="q_{inf}\}" display="inline"><semantics id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1a"><mrow id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1b"><msub id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1"><mi id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2">q</mi><mrow id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3"><mi id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2">i</mi><mo lspace="0em" rspace="0em" id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1">​</mo><mi id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3">n</mi><mo lspace="0em" rspace="0em" id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1a">​</mo><mi id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4">f</mi></mrow></msub><mo stretchy="false" id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.2">}</mo></mrow><annotation encoding="application/x-tex" id="Ch6.F13.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1c">q_{inf}\}</annotation></semantics></math></span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.13: </span>One-Shot AutoDiCot + Full Context</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Ablating Email.</h6>

<div id="Ch6.S2.SS3.SSS3.Px4.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px4.p1.3" class="ltx_p">The results of the previous changes were promising, but they did involve creating a prompt that included information from an email message that had not been created for that purpose, and which included information about the project, the dataset, etc. that were not intended for disclosure to a broad audience. Ironically, though, removing this email significantly brought performance back down, <math id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px4.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px4.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.18</span> (0.18) F1, <math id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px4.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px4.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.22</span>(0.17) precision and <math id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px4.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px4.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.13</span> (0.20) recall. We attribute this to the fact that the email provided richer background information about the goals of the labeling. Although we would not recommend including email or any other potentially identifying information in any LLM prompt, we chose to leave the email in the prompt; this is consistent with scenarios in many typical settings, in which prompts are not expected to be exposed to others.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot + 1 AutoDiCoT.</h6>

<div id="Ch6.S2.SS3.SSS3.Px5.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px5.p1.3" class="ltx_p">As a next step, the prompt engineer tried including full context, 10 regular exemplars, and the one-shot exemplar about how not to reason. This hurt performance (Figure <a href="#Ch6.F14" title="Figure 6.14 ‣ 10-Shot + 1 AutoDiCoT. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.14</span></a>) <math id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px5.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px5.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.30</span> (0.15) F1, <math id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px5.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px5.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.15</span> (0.15) precision, <math id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px5.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px5.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.15</span> (0.15) recall.</p>
</div>
<figure id="Ch6.F14" class="ltx_figure"><svg id="Ch6.F14.pic1" class="ltx_picture ltx_centering" height="307.8" overflow="visible" version="1.1" width="600"><g transform="translate(0,307.8) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 307.8 L 600 307.8 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 305.84 L 598.03 305.84 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="280.25" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.8" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.9" class="ltx_p">{ENTRAPMENT DEFINITION (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>)}</span>
<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.10" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
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<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.11" class="ltx_p">…</span>
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<span id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4" class="ltx_p">A: {<math id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1" class="ltx_Math" alttext="a_{10}" display="inline"><semantics id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1a"><msub id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.cmml"><mi id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2.cmml">a</mi><mn id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3.cmml">10</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1b"><apply id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.cmml" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.1.cmml" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1">subscript</csymbol><ci id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2.cmml" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2">𝑎</ci><cn type="integer" id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3.cmml" xref="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3">10</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F14.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1c">a_{10}</annotation></semantics></math>}</span>
<span id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5" class="ltx_p">Q: {<math id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1" class="ltx_Math" alttext="q_{12}" display="inline"><semantics id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1a"><msub id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.cmml"><mi id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2.cmml">q</mi><mn id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3.cmml">12</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1b"><apply id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.cmml" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.1.cmml" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1">subscript</csymbol><ci id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2.cmml" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2">𝑞</ci><cn type="integer" id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3.cmml" xref="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3">12</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F14.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1c">q_{12}</annotation></semantics></math>}</span>
<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.12" class="ltx_p">R: Although "{LLM REASONING}" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.</span>
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<span id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_p">Q: {<math id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1" class="ltx_Math" alttext="q_{inf}" display="inline"><semantics id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1a"><msub id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.cmml"><mi id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2.cmml">q</mi><mrow id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.cmml"><mi id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2.cmml">i</mi><mo lspace="0em" rspace="0em" id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3.cmml">n</mi><mo lspace="0em" rspace="0em" id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1a" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4.cmml">f</mi></mrow></msub><annotation-xml encoding="MathML-Content" id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1b"><apply id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.1.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1">subscript</csymbol><ci id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2">𝑞</ci><apply id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3"><times id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1"></times><ci id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2">𝑖</ci><ci id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3">𝑛</ci><ci id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4.cmml" xref="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4">𝑓</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F14.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1c">q_{inf}</annotation></semantics></math>}</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.14: </span>10-Shot + 1 AutoDiCoT</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px6" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Full Context Only.</h6>

<div id="Ch6.S2.SS3.SSS3.Px6.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px6.p1.3" class="ltx_p">Next, a prompt was created using only full context, without any exemplars (Figure <a href="#Ch6.F15" title="Figure 6.15 ‣ Full Context Only. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.15</span></a>). This boosted performance over the previous technique, <math id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px6.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px6.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.01</span> (0.44) F1, <math id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px6.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px6.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.01</span> (0.29) precision, <math id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px6.p1.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px6.p1.3.3" class="ltx_text" style="color:#00FF00;"> 0.62</span> (0.92) recall. Interestingly, in this prompt, the prompt engineer accidentally pasted in the full-context email twice, and that ended up having significant positive effects on performance later (and removing the duplicate actually decreased performance). This is reminiscent of the re-reading technique <cite class="ltx_cite ltx_citemacro_cite">Xu et al. (<a href="#bib.bib317" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
<div id="Ch6.S2.SS3.SSS3.Px6.p2" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px6.p2.1" class="ltx_p">This can be interpreted both optimistically and pessimistically. Optimistically, it demonstrates how improvements can arise through exploration and fortuitous discovery. On the pessimistic side, the value of duplicating the email in the prompt highlights the extent to which prompting remains a difficult to explain black art, where the LLM may turn out to be unexpectedly sensitive to variations one might not expect to matter.</p>
</div>
<figure id="Ch6.F15" class="ltx_figure"><svg id="Ch6.F15.pic1" class="ltx_picture ltx_centering" height="174.97" overflow="visible" version="1.1" width="600"><g transform="translate(0,174.97) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 174.97 L 600 174.97 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 173 L 598.03 173 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="147.41" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.2" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.3" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.4" class="ltx_p">{ENTRAPMENT DEFINITION (Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>)}</span>
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.5" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
<span id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_p">Q: {<math id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="q_{inf}" display="inline"><semantics id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1a"><msub id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml"><mi id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml">q</mi><mrow id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml"><mi id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2.cmml">i</mi><mo lspace="0em" rspace="0em" id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3.cmml">n</mi><mo lspace="0em" rspace="0em" id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1a" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1.cmml">​</mo><mi id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.4" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.4.cmml">f</mi></mrow></msub><annotation-xml encoding="MathML-Content" id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1b"><apply id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1">subscript</csymbol><ci id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2">𝑞</ci><apply id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3"><times id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.1"></times><ci id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.2">𝑖</ci><ci id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.3">𝑛</ci><ci id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.4.cmml" xref="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.4">𝑓</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F15.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1c">q_{inf}</annotation></semantics></math>}
A:</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.15: </span>Full Context Only</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px7" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot AutoDiCoT.</h6>

<div id="Ch6.S2.SS3.SSS3.Px7.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px7.p1.3" class="ltx_p">The next step was to create more AutoDiCoT exemplars, per the algorithm in Figure <a href="#Ch6.F12" title="Figure 6.12 ‣ One-Shot AutoDiCot + Full Context. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.12</span></a>. A total of ten new AutoDiCoT exemplars were added to the full context prompt (Figure <a href="#Ch6.F16" title="Figure 6.16 ‣ 10-Shot AutoDiCoT. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.16</span></a>). This yielded the most successful prompt from this prompt engineering exercise, in terms of F1 score, <math id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px7.p1.1.m1.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px7.p1.3.1" class="ltx_text" style="color:#00FF00;"> 0.08</span> (0.53) F1, <math id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px7.p1.2.m2.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px7.p1.3.2" class="ltx_text" style="color:#00FF00;"> 0.08</span> (0.38) precision, <math id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px7.p1.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px7.p1.3.3" class="ltx_text" style="color:#00FF00;"> 0.53</span> (0.86) recall.</p>
</div>
<figure id="Ch6.F16" class="ltx_figure"><svg id="Ch6.F16.pic1" class="ltx_picture ltx_centering" height="274.6" overflow="visible" version="1.1" width="600"><g transform="translate(0,274.6) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 274.6 L 600 274.6 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 272.63 L 598.03 272.63 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="247.04" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F16.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F16.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.8" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F16.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.9" class="ltx_p">{ENTRAPMENT DEFINITION}</span>
<span id="Ch6.F16.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.10" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
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</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.16: </span>10-Shot AutoDiCoT</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px8" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">20-Shot AutoDiCoT.</h6>

<div id="Ch6.S2.SS3.SSS3.Px8.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px8.p1.3" class="ltx_p">Further experimentation proceeded seeking (unsuccesfully) to improve on the previous F1 result. In one attempt, the prompt engineer labeled an additional ten exemplars, and created a 20-shot prompt from the first 20 data points in the development set. This led to worse results than the 10-shot prompt, when tested on all samples other than the first twenty, <math id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px8.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px8.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.04</span> (0.49) F1, <math id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px8.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px8.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision, <math id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px8.p1.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px8.p1.3.3" class="ltx_text" style="color:#00FF00;"> 0.08</span> (0.94) recall. Notably, it also yielded worse performance on the test set.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px9" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">20-Shot AutoDiCoT + Full Words.</h6>

<div id="Ch6.S2.SS3.SSS3.Px9.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px9.p1.3" class="ltx_p">The prompt engineer conjectured that the LLM would perform better if the prompt included full words <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.1" class="ltx_emph ltx_font_italic">Question</em>, <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.2" class="ltx_emph ltx_font_italic">Reasoning</em>, and <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.3" class="ltx_emph ltx_font_italic">Answer</em> rather than <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.4" class="ltx_emph ltx_font_italic">Q</em>, <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.5" class="ltx_emph ltx_font_italic">R</em>, <em id="Ch6.S2.SS3.SSS3.Px9.p1.3.6" class="ltx_emph ltx_font_italic">A</em>. However, this did not succeed (Figure <a href="#Ch6.F17" title="Figure 6.17 ‣ 20-Shot AutoDiCoT + Full Words. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.17</span></a>), <math id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px9.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px9.p1.3.7" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.48) F1, <math id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px9.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px9.p1.3.8" class="ltx_text" style="color:#FF0000;"> 0.06</span> (0.32) precision, <math id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1" class="ltx_Math" alttext="\uparrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1a"><mo mathcolor="#00FF00" stretchy="false" id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1.1.cmml">↑</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1.1">↑</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px9.p1.3.m3.1c">\uparrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px9.p1.3.9" class="ltx_text" style="color:#00FF00;"> 0.08</span> (0.94) recall.</p>
</div>
<figure id="Ch6.F17" class="ltx_figure"><svg id="Ch6.F17.pic1" class="ltx_picture ltx_centering" height="274.6" overflow="visible" version="1.1" width="600"><g transform="translate(0,274.6) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 274.6 L 600 274.6 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 272.63 L 598.03 272.63 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="247.04" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.8" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.9" class="ltx_p">{ENTRAPMENT DEFINITION}</span>
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.10" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
<span id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1" class="ltx_p">Question: {<math id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="q_{1}" display="inline"><semantics id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1a"><msub id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml"><mi id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml">q</mi><mn id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml">1</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1b"><apply id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.cmml" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.1.cmml" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2.cmml" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.2">𝑞</ci><cn type="integer" id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3.cmml" xref="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.m1.1c">q_{1}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2" class="ltx_p">Reasoning: {<math id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1" class="ltx_Math" alttext="r_{1}" display="inline"><semantics id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1a"><msub id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.cmml"><mi id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.2" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.2.cmml">r</mi><mn id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.3" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.3.cmml">1</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1b"><apply id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.cmml" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.1.cmml" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.2.cmml" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.2">𝑟</ci><cn type="integer" id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.3.cmml" xref="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.m1.1c">r_{1}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3" class="ltx_p">Answer: {<math id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1" class="ltx_Math" alttext="a_{1}" display="inline"><semantics id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1a"><msub id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.cmml"><mi id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.2" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.2.cmml">a</mi><mn id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.3" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.3.cmml">1</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1b"><apply id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.cmml" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.1.cmml" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.2.cmml" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.2">𝑎</ci><cn type="integer" id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.3.cmml" xref="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.m1.1c">a_{1}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.11" class="ltx_p">…</span>
<span id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4" class="ltx_p">Question: {<math id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1" class="ltx_Math" alttext="q_{20}" display="inline"><semantics id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1a"><msub id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.cmml"><mi id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2.cmml">q</mi><mn id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3.cmml">20</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1b"><apply id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.cmml" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.1.cmml" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2.cmml" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.2">𝑞</ci><cn type="integer" id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3.cmml" xref="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1.1.3">20</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.4.m1.1c">q_{20}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5" class="ltx_p">Reasoning: {<math id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1" class="ltx_Math" alttext="r_{20}" display="inline"><semantics id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1a"><msub id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.cmml"><mi id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2.cmml">r</mi><mn id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3.cmml">20</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1b"><apply id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.cmml" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.1.cmml" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2.cmml" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.2">𝑟</ci><cn type="integer" id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3.cmml" xref="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1.1.3">20</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5.m1.1c">r_{20}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6" class="ltx_p">Answer: {<math id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1" class="ltx_Math" alttext="a_{20}" display="inline"><semantics id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1a"><msub id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.cmml"><mi id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.2" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.2.cmml">a</mi><mn id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.3" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.3.cmml">20</mn></msub><annotation-xml encoding="MathML-Content" id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1b"><apply id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.cmml" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1"><csymbol cd="ambiguous" id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.1.cmml" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1">subscript</csymbol><ci id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.2.cmml" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.2">𝑎</ci><cn type="integer" id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.3.cmml" xref="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1.1.3">20</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.F17.pic1.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.6.m1.1c">a_{20}</annotation></semantics></math>}</span>
<span id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7" class="ltx_p">Question: {<math id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1" class="ltx_math_unparsed" alttext="q_{inf}\}" display="inline"><semantics id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1a"><mrow id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1b"><msub id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1"><mi id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.2">q</mi><mrow id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3"><mi id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.2">i</mi><mo lspace="0em" rspace="0em" id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1">​</mo><mi id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.3">n</mi><mo lspace="0em" rspace="0em" id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.1a">​</mo><mi id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.1.3.4">f</mi></mrow></msub><mo stretchy="false" id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1.2">}</mo></mrow><annotation encoding="application/x-tex" id="Ch6.F17.pic1.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.7.m1.1c">q_{inf}\}</annotation></semantics></math></span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.17: </span>20-shot AutoDiCoT</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px10" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">20-Shot AutoDiCoT + Full Words + Extraction Prompt.</h6>

<div id="Ch6.S2.SS3.SSS3.Px10.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px10.p1.2" class="ltx_p">The prompt engineer then noticed that in many cases, the LLM generated outputs that could not properly be parsed to obtain a response. So, they crafted a prompt that extracted answers from the LLM’s response (Figure <a href="#Ch6.F18" title="Figure 6.18 ‣ 10-Shot AutoDiCoT + Extraction Prompt. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.18</span></a>). Although this improved accuracy by a few points, it decreased F1, thanks to the fact that many of the outputs that had been unparsed actually contained incorrect responses, <math id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px10.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px10.p1.2.1" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.48) F1, <math id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px10.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px10.p1.2.2" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision, with no change in recall (0.86).</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px11" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot AutoDiCoT + Extraction Prompt.</h6>

<div id="Ch6.S2.SS3.SSS3.Px11.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px11.p1.3" class="ltx_p">Applying the extraction prompt to the best performing 10-Shot AutoDiCoT prompt did not improve results, <math id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px11.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px11.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.04</span> (0.49) F1, <math id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px11.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px11.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.06</span> (0.78) recall, <math id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px11.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px11.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.03</span> (0.35) precision.</p>
</div>
<figure id="Ch6.F18" class="ltx_figure"><svg id="Ch6.F18.pic1" class="ltx_picture ltx_centering" height="236.42" overflow="visible" version="1.1" width="600"><g transform="translate(0,236.42) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 236.42 L 600 236.42 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 234.45 L 598.03 234.45 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="208.86" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="Ch6.F18.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="Ch6.F18.pic1.1.1.1.1.1.1" class="ltx_p">{PROFESSOR’s EMAIL}</span>
<span id="Ch6.F18.pic1.1.1.1.1.1.2" class="ltx_p">{ENTRAPMENT DEFINITION}</span>
<span id="Ch6.F18.pic1.1.1.1.1.1.3" class="ltx_p">IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.</span>
<span id="Ch6.F18.pic1.1.1.1.1.1.4" class="ltx_p">Question: {REDACTED}</span>
<span id="Ch6.F18.pic1.1.1.1.1.1.5" class="ltx_p">Answer: {ANSWER}</span>
<span id="Ch6.F18.pic1.1.1.1.1.1.6" class="ltx_p">Does this Answer indicate entrapment? Output the word Yes if it is labeled as entrapment and output the word No if it is not labeled as entrapment. Only output the word Yes or the word No.</span>
</span></foreignObject></g></g></svg>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.18: </span>Extraction Prompt</figcaption>
</figure>
</section>
<section id="Ch6.S2.SS3.SSS3.Px12" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot AutoDiCoT without Email.</h6>

<div id="Ch6.S2.SS3.SSS3.Px12.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px12.p1.3" class="ltx_p">As noted above, removing the email outright from the prompt hurt performance, <math id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px12.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px12.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.14</span> (0.39) F1, <math id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px12.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px12.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.38</span> (0.48) recall, <math id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px12.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px12.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px13" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">De-Duplicating Email.</h6>

<div id="Ch6.S2.SS3.SSS3.Px13.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px13.p1.3" class="ltx_p">Also as noted above, it seemed reasonable that removing the duplication of the email would perform as well or better than the prompt with the unintentional duplication. As it turned out, however, removing the duplicate significantly hurt performance, <math id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px13.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px13.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.07</span> (0.45) F1, <math id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px13.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px13.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.13</span> (0.73) recall, <math id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px13.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px13.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px14" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot AutoDiCoT + Default to Negative.</h6>

<div id="Ch6.S2.SS3.SSS3.Px14.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px14.p1.3" class="ltx_p">This approach used the best performing prompt, and defaulted to labeling as negative (not entrapment) in the case of answers that are not extracted properly. This did not help performance, <math id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px14.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px14.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.11</span> (0.42) F1, <math id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px14.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px14.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.03</span> (0.83) recall, <math id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px14.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px14.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.10</span> (0.28) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px15" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Ensemble + Extraction.</h6>

<div id="Ch6.S2.SS3.SSS3.Px15.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px15.p1.3" class="ltx_p">Especially for systems that are sensitive to the details of their inputs, there are advantages in trying multiple variations of an input and then combining their results. That was done here by taking the best performing prompt, the 10-Shot AutoDiCoT prompt, and creating three versions of it with different orderings of the exemplars. The average of the three results was taken to be the final answer. Unfortunately, both orderings that differed from the default ordering led to the LLM not outputting a well structured response. An extraction prompt was therefore used to obtain final answers. This exploration hurt rather than helped performance <math id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px15.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px15.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.16</span> (0.36) F1, <math id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px15.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px15.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.22</span> (0.64) recall, <math id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px15.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px15.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.12</span> (0.26) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px16" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">10-Shot AutoCoT + 3x the context (no email dupe).</h6>

<div id="Ch6.S2.SS3.SSS3.Px16.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px16.p1.3" class="ltx_p">Recall that <em id="Ch6.S2.SS3.SSS3.Px16.p1.3.1" class="ltx_emph ltx_font_italic">context</em> refers to the description of entrapment, an instruction about explicitness, and an email. Since the duplicated email had improved performance, the prompt engineer tested out pasting in three copies of the context (first de-duplicating the email). However, this did not improve performance, <math id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px16.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px16.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.06</span> (0.47) F1, <math id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px16.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px16.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.08</span> (0.78) recall, <math id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px16.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px16.p1.3.4" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px17" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Anonymize Email.</h6>

<div id="Ch6.S2.SS3.SSS3.Px17.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px17.p1.3" class="ltx_p">At this point it seemed clear that including the duplicated email in the prompt was actually, although not explainably, essential to the best performance so far obtained. The prompt engineer decided to anonymize the email by replacing personal names with other, random names. However, surprisingly, this decreased performance significantly <math id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1b"><ci id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px17.p1.1.m1.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px17.p1.3.1" class="ltx_text" style="color:#FF0000;"> 0.08</span> (0.45) F1, <math id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1b"><ci id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px17.p1.2.m2.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px17.p1.3.2" class="ltx_text" style="color:#FF0000;"> 0.14</span> (0.72) recall, <math id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1" class="ltx_Math" alttext="\downarrow" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1a"><mo mathcolor="#FF0000" stretchy="false" id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1.1" xref="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1.1.cmml">↓</mo><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1b"><ci id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1.1">↓</ci></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px17.p1.3.m3.1c">\downarrow</annotation></semantics></math><span id="Ch6.S2.SS3.SSS3.Px17.p1.3.3" class="ltx_text" style="color:#FF0000;"> 0.05</span> (0.33) precision.</p>
</div>
</section>
<section id="Ch6.S2.SS3.SSS3.Px18" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">DSPy.</h6>

<div id="Ch6.S2.SS3.SSS3.Px18.p1" class="ltx_para">
<p id="Ch6.S2.SS3.SSS3.Px18.p1.2" class="ltx_p">We concluded the case study by exploring an alternative to manual prompt engineering, the DSPy framework <cite class="ltx_cite ltx_citemacro_cite">Khattab et al. (<a href="#bib.bib117" title="" class="ltx_ref">2023</a>)</cite>, which automatically optimizes LLM prompts for a given target metric.
Specifically, we begin with a chain-of-thought classification pipeline that uses the definition of entrapment in Figure <a href="#Ch6.F7" title="Figure 6.7 ‣ 6.2.3.1 Dataset Exploration (2 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.7</span></a>. Over 16 iterations, DSPy bootstrapped synthetic LLM-generated demonstrations and randomly sampled training exemplars, with the ultimate objective of maximizing <math id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1" class="ltx_Math" alttext="F1" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1a"><mrow id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.2" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.2.cmml">F</mi><mo lspace="0em" rspace="0em" id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.1" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.1.cmml">​</mo><mn id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.3" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.3.cmml">1</mn></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1b"><apply id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1"><times id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.1"></times><ci id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.2">𝐹</ci><cn type="integer" id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px18.p1.1.m1.1c">F1</annotation></semantics></math> on the same development set used above.
We used <span id="Ch6.S2.SS3.SSS3.Px18.p1.2.1" class="ltx_text ltx_font_typewriter">gpt-4-0125-preview</span> and the default settings for the <span id="Ch6.S2.SS3.SSS3.Px18.p1.2.2" class="ltx_text ltx_font_typewriter">BootstrapFewShotWithRandomSearch</span> “teleprompter” (the optimization approach).
Figure <a href="#Ch6.F19" title="Figure 6.19 ‣ DSPy. ‣ 6.2.3.3 Prompting Techniques (32 steps) ‣ 6.2.3 The Process ‣ 6.2 Prompt Engineering Case Study ‣ 6 Benchmarking ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">6.19</span></a> shows the results of two of these prompts on the test set, one of which used default DSPy behaviour, and the second which was manually modified slightly from this default.
The best resulting prompt includes 15 exemplars (without CoT reasoning) and one bootstrapped reasoning demonstration. It achieves 0.548 <math id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1" class="ltx_Math" alttext="F1" display="inline"><semantics id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1a"><mrow id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.cmml"><mi id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.2" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.2.cmml">F</mi><mo lspace="0em" rspace="0em" id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.1" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.1.cmml">​</mo><mn id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.3" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.3.cmml">1</mn></mrow><annotation-xml encoding="MathML-Content" id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1b"><apply id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1"><times id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.1.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.1"></times><ci id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.2.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.2">𝐹</ci><cn type="integer" id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.3.cmml" xref="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1.1.3">1</cn></apply></annotation-xml><annotation encoding="application/x-tex" id="Ch6.S2.SS3.SSS3.Px18.p1.2.m2.1c">F1</annotation></semantics></math> (and 0.385 / 0.952 precision / recall) on the test set, without making any use of the professor’s email nor the incorrect instruction about the explicitness of entrapment. It also performs much better than the human prompt engineer’s prompts on the test set, which demonstrates the significant promise of automated prompt engineering.</p>
</div>
<figure id="Ch6.F19" class="ltx_figure"><img src="/html/2406.06608/assets/x12.png" id="Ch6.F19.g1" class="ltx_graphics ltx_centering ltx_img_landscape" width="461" height="343" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure 6.19: </span>Scores of different prompting techniques on the test set.</figcaption>
</figure>
</section>
</section>
</section>
<section id="Ch6.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">6.2.4 </span>Discussion</h4>

<div id="Ch6.S2.SS4.p1" class="ltx_para">
<p id="Ch6.S2.SS4.p1.1" class="ltx_p">Prompt engineering is a non-trivial process, the nuances of which are not currently well described in literature.
From the fully manual process illustrated above, there are several take-aways worth summarizing. First, prompt engineering is fundamentally different from other ways of getting a computer to behave the way you want it to: these systems are being cajoled, not programmed, and, in addition to being quite sensitive to the specific LLM being used, they can be incredibly sensitive to specific details in prompts without there being any obvious reason those details should matter. Second, therefore, it is important to dig into the data (e.g. generating potential explanations for LLM “reasoning” that leads to incorrect responses). Related, the third and most important take-away is that prompt engineering should involve engagement between the prompt engineer, who has expertise in how to coax LLMs to behave in desired ways, and domain experts, who understand what those desired ways are and why.</p>
</div>
<div id="Ch6.S2.SS4.p2" class="ltx_para">
<p id="Ch6.S2.SS4.p2.1" class="ltx_p">Ultimately we found that there was significant promise in an automated method for exploring the prompting space, but also that combining that automation with human prompt engineering/revision was the most successful approach. We hope that this study will serve as a step toward more robust examinations of how to perform prompt engineering.</p>
</div>
</section>
</section>
</section>
<section id="Ch7" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">7   Related Work</h2>

<div id="Ch7.p1" class="ltx_para ltx_noindent">
<p id="Ch7.p1.1" class="ltx_p">In this section, we review existing surveys and meta-analyses of prompting.
<cite class="ltx_cite ltx_citemacro_citet">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite> perform a systematic review of prompt engineering in the pre-ChatGPT era, including various aspects of prompting like prompt template engineering, answer engineering, prompt ensembling, and prompt tuning methods.
Their review covers many different types of prompting (e.g., cloze, soft-prompting, etc., across many different types of language models) while we focus on discrete pre-fix prompting but more in-depth discussion.
<cite class="ltx_cite ltx_citemacro_citet">Chen et al. (<a href="#bib.bib29" title="" class="ltx_ref">2023a</a>)</cite> provide a review of popular prompting techniques like Chain-of-Thought, Tree-of-Thought, Self-Consistency, and Least-to-Most prompting, along with outlooks for future prompting research.
<cite class="ltx_cite ltx_citemacro_citet">White et al. (<a href="#bib.bib305" title="" class="ltx_ref">2023</a>)</cite> and <cite class="ltx_cite ltx_citemacro_citet">Schmidt et al. (<a href="#bib.bib245" title="" class="ltx_ref">2023</a>)</cite> provide a taxonomy of prompt patterns, which are similar to software patterns (and prompting techniques for that matter).
<cite class="ltx_cite ltx_citemacro_citet">Gao (<a href="#bib.bib63" title="" class="ltx_ref">2023</a>)</cite> provide a practical prompting technique tutorial for a non-technical audience.
<cite class="ltx_cite ltx_citemacro_citet">Santu and Feng (<a href="#bib.bib240" title="" class="ltx_ref">2023</a>)</cite> provide a general taxonomy of prompts that can be used to design prompts with
specific properties to perform a wide
range of complex tasks.
<cite class="ltx_cite ltx_citemacro_citet">Bubeck et al. (<a href="#bib.bib23" title="" class="ltx_ref">2023</a>)</cite> qualitatively experiment with a wide range of prompting methods on the early version of GPT-4 to understand its capabilities.
<cite class="ltx_cite ltx_citemacro_citet">Chu et al. (<a href="#bib.bib39" title="" class="ltx_ref">2023</a>)</cite> review Chain-of-Thought related prompting methods for reasoning.
In earlier work, <cite class="ltx_cite ltx_citemacro_citet">Bommasani et al. (<a href="#bib.bib18" title="" class="ltx_ref">2021</a>)</cite> review and discuss opportunities and risks of foundation models broadly, and
<cite class="ltx_cite ltx_citemacro_citet">Dang et al. (<a href="#bib.bib43" title="" class="ltx_ref">2022</a>)</cite> discuss prompting strategies for interactive creative applications that use prompting as a new paradigm for human interaction, with a particular focus on the user interface design that supports user prompting.
As an addition to these existing surveys, our review aims to provide a more updated and formalized systematic review.</p>
</div>
<div id="Ch7.p2" class="ltx_para">
<p id="Ch7.p2.1" class="ltx_p">There is also a line of work that surveys prompting techniques for particular domains or downstream applications.
<cite class="ltx_cite ltx_citemacro_citet">Meskó (<a href="#bib.bib181" title="" class="ltx_ref">2023</a>)</cite> and <cite class="ltx_cite ltx_citemacro_citet">Wang et al. (<a href="#bib.bib288" title="" class="ltx_ref">2023d</a>)</cite> offer recommended use cases and limitations of prompt engineering in the medical and healthcare domains.
<cite class="ltx_cite ltx_citemacro_citet">Heston and Khun (<a href="#bib.bib92" title="" class="ltx_ref">2023</a>)</cite> provide a review of prompt engineering for medical education use cases.
<cite class="ltx_cite ltx_citemacro_citet">Peskoff and Stewart (<a href="#bib.bib209" title="" class="ltx_ref">2023</a>)</cite> query ChatGPT and YouChat to assess domain coverage.
<cite class="ltx_cite ltx_citemacro_citet">Hua et al. (<a href="#bib.bib96" title="" class="ltx_ref">2024</a>)</cite> use a GPT-4-automated approach to review LLMs in the mental health space.
<cite class="ltx_cite ltx_citemacro_citet">Wang et al. (<a href="#bib.bib287" title="" class="ltx_ref">2023c</a>)</cite> review prompt engineering and relevant models in the visual modality and <cite class="ltx_cite ltx_citemacro_citet">Yang et al. (<a href="#bib.bib323" title="" class="ltx_ref">2023e</a>)</cite> provided a comprehensive list of qualitative analyses of multimodal prompting, particularly focusing on GPT-4V<span id="Ch7.footnote19" class="ltx_note ltx_role_footnote"><sup class="ltx_note_mark">19</sup><span class="ltx_note_outer"><span class="ltx_note_content"><sup class="ltx_note_mark">19</sup><span class="ltx_tag ltx_tag_note">19</span><a target="_blank" href="https://openai.com/research/gpt-4v-system-card" title="" class="ltx_ref ltx_url ltx_font_typewriter">https://openai.com/research/gpt-4v-system-card</a></span></span></span>.
<cite class="ltx_cite ltx_citemacro_citet">Durante et al. (<a href="#bib.bib52" title="" class="ltx_ref">2024</a>)</cite> review multimodal interactions based on LLM embodied agents.
<cite class="ltx_cite ltx_citemacro_citet">Ko et al. (<a href="#bib.bib124" title="" class="ltx_ref">2023b</a>)</cite> review literature on the adoption of Text-to-Image generation models for visual artists’ creative works. <cite class="ltx_cite ltx_citemacro_citet">Gupta et al. (<a href="#bib.bib81" title="" class="ltx_ref">2024</a>)</cite> review GenAI through a topic modeling approach.
<cite class="ltx_cite ltx_citemacro_citet">Awais et al. (<a href="#bib.bib8" title="" class="ltx_ref">2023</a>)</cite> review foundation models in vision, including various prompting techniques.
<cite class="ltx_cite ltx_citemacro_citet">Hou et al. (<a href="#bib.bib94" title="" class="ltx_ref">2023</a>)</cite> perform a systematic review of prompt engineering techniques as they relate to software engineering. They use a systematic review technique developed by <cite class="ltx_cite ltx_citemacro_citet">Keele et al. (<a href="#bib.bib111" title="" class="ltx_ref">2007</a>)</cite>, specifically for software engineering reviews. <cite class="ltx_cite ltx_citemacro_citet">Wang et al. (<a href="#bib.bib289" title="" class="ltx_ref">2023e</a>)</cite> review the literature on software testing with large language models.
<cite class="ltx_cite ltx_citemacro_citet">Zhang et al. (<a href="#bib.bib338" title="" class="ltx_ref">2023a</a>)</cite> review ChatGPT prompting performance on software engineering tasks such as automated program repair.
<cite class="ltx_cite ltx_citemacro_citet">Neagu (<a href="#bib.bib193" title="" class="ltx_ref">2023</a>)</cite> provide a systematic review on how prompt engineering can be leveraged in computer science education.
<cite class="ltx_cite ltx_citemacro_citet">Li et al. (<a href="#bib.bib151" title="" class="ltx_ref">2023j</a>)</cite> review literature on the fairness of large language models.
There are also surveys on related aspects such as hallucination of language models <cite class="ltx_cite ltx_citemacro_cite">Huang et al. (<a href="#bib.bib99" title="" class="ltx_ref">2023b</a>)</cite>, verifiability <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib158" title="" class="ltx_ref">2023a</a>)</cite>, reasoning <cite class="ltx_cite ltx_citemacro_cite">Qiao et al. (<a href="#bib.bib224" title="" class="ltx_ref">2022</a>)</cite>, augmentation <cite class="ltx_cite ltx_citemacro_cite">Mialon et al. (<a href="#bib.bib183" title="" class="ltx_ref">2023</a>)</cite>, and linguistic properties of prompts <cite class="ltx_cite ltx_citemacro_cite">Leidinger et al. (<a href="#bib.bib134" title="" class="ltx_ref">2023</a>)</cite>.
Different from these works, we perform our review targeting broad coverage and generally applicable prompting techniques.
Finally, in terms of more general prior surveys <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>); Sahoo et al. (<a href="#bib.bib238" title="" class="ltx_ref">2024</a>)</cite>, this survey offers an update in a fast-moving field. In addition, we provide a starting point for taxonomic organization of prompting techniques and standardization of terminology. Moreover, we base our work in the widely well-received standard for systematic literature reviews — PRISMA <cite class="ltx_cite ltx_citemacro_cite">Page et al. (<a href="#bib.bib199" title="" class="ltx_ref">2021</a>)</cite>.</p>
</div>
</section>
<section id="Ch8" class="ltx_chapter">
<h2 class="ltx_title ltx_font_bold ltx_title_chapter">8   Conclusions</h2>

<div id="Ch8.p1" class="ltx_para ltx_noindent">
<p id="Ch8.p1.1" class="ltx_p">Generative AI is a novel technology, and broader understanding of models’ capabilities and limitations remains limited.
Natural language is a flexible, open-ended interface, with models having few obvious affordances.
The use of Generative AI therefore inherits many of the standard challenges of linguistic communication—e.g., ambiguity, the role of context, the need for course correction—while at the same time adding the challenge of communicating with an entity whose “understanding” of language may not bear any substantial relationship to human understanding.
Many of the techniques described here have been called “emergent”, but it is perhaps more appropriate to say that they were <em id="Ch8.p1.1.1" class="ltx_emph ltx_font_italic">discovered</em>—the result of thorough experimentation, analogies from human reasoning, or pure serendipity.</p>
</div>
<div id="Ch8.p2" class="ltx_para">
<p id="Ch8.p2.1" class="ltx_p">The present work is an initial attempt to categorize the species of an unfamiliar territory.
While we make every attempt to be comprehensive, there are sure to be gaps and redundancies.
Our intention is to provide a taxonomy and terminology that cover a large number of existing prompt engineering techniques, and which can accommodate future methods.
We discuss over 200 prompting techniques, frameworks built around them, and issues like safety and security that need to be kept in mind when using them.
We also present two case studies in order to provide a clear sense of models’ capabilities and what it is like to tackle a problem in practice.
Last, our stance is primarily observational, and we make no claims to the validity of the presented techniques.
The field is new, and evaluation is variable and unstandardized—even the most meticulous experimentation may suffer from unanticipated shortcomings, and model outputs themselves are sensitive to meaning-preserving changes in inputs.
As a result, we encourage the reader to avoid taking any claims at face value and to recognize that techniques may not transfer to other models, problems, or datasets.</p>
</div>
<div id="Ch8.p3" class="ltx_para">
<p id="Ch8.p3.1" class="ltx_p">To those just beginning in prompt engineering, our recommendations resemble what one would recommend in any machine learning setting: understand the <em id="Ch8.p3.1.1" class="ltx_emph ltx_font_italic">problem</em> you are trying to solve (rather than just focusing on input/output and benchmark scores), and ensure the data and metrics you are working with constitute a good representation of that problem.
It is better to start with simpler approaches first, and to remain skeptical of claims about method performance.
To those already engaged in prompt engineering, we hope that our taxonomy will shed light on the relationships between existing techniques.
To those developing new techniques, we encourage situating new methods within our taxonomy, as well as including ecologically valid case studies and illustrations of those techniques.</p>
</div>
<section id="Ch8.Sx1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">Acknowledgements</h3>

<div id="Ch8.Sx1.p1" class="ltx_para">
<p id="Ch8.Sx1.p1.1" class="ltx_p">We appreciate the advice given by Hal Daumé III, Adam Visokay, and Jordan Boyd-Graber and review by Diyi Yang and Brandon M. Stewart. We also appreciate the 10K USD in API credits given by OpenAI and design work by Benjamin DiMarco.</p>
</div>
<div class="ltx_pagination ltx_role_newpage"></div>
</section>
</section>
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<div class="ltx_pagination ltx_role_newpage"></div>
<section id="A1" class="ltx_appendix">
<h2 class="ltx_title ltx_title_appendix">
<span class="ltx_tag ltx_tag_appendix">Appendix A </span>Appendices</h2>

<section id="A1.S1" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.1 </span>Definitions of Prompting</h3>

<div id="A1.S1.p1" class="ltx_para">
<table id="A1.S1.p1.1" class="ltx_tabular ltx_align_middle">
<tr id="A1.S1.p1.1.2" class="ltx_tr">
<td id="A1.S1.p1.1.2.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.2.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.2.1.1.1" class="ltx_p" style="width:56.9pt;">Reference</span>
</span>
</td>
<td id="A1.S1.p1.1.2.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.2.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.2.2.1.1" class="ltx_p">Prompt</span>
</span>
</td>
<td id="A1.S1.p1.1.2.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.2.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.2.3.1.1" class="ltx_p">Prompt Engineering</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.3" class="ltx_tr">
<td id="A1.S1.p1.1.3.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.3.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.3.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Meskó (<a href="#bib.bib181" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.3.2" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.3.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.3.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.3.3.1.1" class="ltx_p">The practice of designing, refining, and implementing prompts or instructions that guide the output of LLMs to help in various tasks. It is essentially the practice of effectively interacting with AI systems to optimize their benefits.</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.4" class="ltx_tr">
<td id="A1.S1.p1.1.4.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.4.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.4.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. (<a href="#bib.bib29" title="" class="ltx_ref">2023a</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.4.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.4.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.4.2.1.1" class="ltx_p">the input of the model</span>
</span>
</td>
<td id="A1.S1.p1.1.4.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.4.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.4.3.1.1" class="ltx_p">the process of structuring input text for LLMs and is a technique integral to optimizing
the efficacy of LLMs</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.5" class="ltx_tr">
<td id="A1.S1.p1.1.5.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.5.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.5.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Santu and Feng (<a href="#bib.bib240" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.5.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.5.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.5.2.1.1" class="ltx_p">refers to a textual input provided to the LLMs
with the intention of guiding its output toward a
specific task</span>
</span>
</td>
<td id="A1.S1.p1.1.5.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.5.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.5.3.1.1" class="ltx_p">involves crafting and
revising the query or context in such a way that
it elicits the desired response or behavior from
LLMs</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.6" class="ltx_tr">
<td id="A1.S1.p1.1.6.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.6.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.6.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib288" title="" class="ltx_ref">2023d</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.6.2" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.6.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.6.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.6.3.1.1" class="ltx_p">involves designing effective prompts to guide the pre-trained language model in
downstream tasks.</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.7" class="ltx_tr">
<td id="A1.S1.p1.1.7.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.7.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.7.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib287" title="" class="ltx_ref">2023c</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.7.2" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.7.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.7.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.7.3.1.1" class="ltx_p">the process of designing prompts that enable the model to adapt and generalize to different tasks.
downstream tasks.</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.8" class="ltx_tr">
<td id="A1.S1.p1.1.8.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.8.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.8.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Hou et al. (<a href="#bib.bib94" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.8.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.8.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.8.2.1.1" class="ltx_p">manually predefined
natural language instructions</span>
</span>
</td>
<td id="A1.S1.p1.1.8.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.8.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.8.3.1.1" class="ltx_p">the careful design of specialized prompts</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.9" class="ltx_tr">
<td id="A1.S1.p1.1.9.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.9.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.9.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. (<a href="#bib.bib289" title="" class="ltx_ref">2023e</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.9.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.9.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.9.2.1.1" class="ltx_p">input of the LLMs</span>
</span>
</td>
<td id="A1.S1.p1.1.9.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.9.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.9.3.1.1" class="ltx_p">communicate with LLMs to steer its behavior for desired outcomes</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.10" class="ltx_tr">
<td id="A1.S1.p1.1.10.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.10.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.10.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">White et al. (<a href="#bib.bib305" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.10.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.10.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.10.2.1.1" class="ltx_p">Instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM.</span>
<span id="A1.S1.p1.1.10.2.1.2" class="ltx_p">A prompt is a set of instructions provided to an LLM that programs the LLM by customizing it and/or en- hancing or refining its capabilities</span>
</span>
</td>
<td id="A1.S1.p1.1.10.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.10.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.10.3.1.1" class="ltx_p">an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT</span>
<span id="A1.S1.p1.1.10.3.1.2" class="ltx_p">the means by which LLMs are programmed via prompts</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.11" class="ltx_tr">
<td id="A1.S1.p1.1.11.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.11.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.11.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Heston and Khun (<a href="#bib.bib92" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.11.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.11.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.11.2.1.1" class="ltx_p">the input</span>
</span>
</td>
<td id="A1.S1.p1.1.11.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.11.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.11.3.1.1" class="ltx_p">structuring the input in a specialized manner</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.1" class="ltx_tr">
<td id="A1.S1.p1.1.1.2" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.1.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.1.2.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.1.3" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.1.1" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.1.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.1.1.1.2" class="ltx_p">choosing a proper prompt</span>
<span id="A1.S1.p1.1.1.1.1.1" class="ltx_p">the process of creating a prompting function <math id="A1.S1.p1.1.1.1.1.1.m1.1" class="ltx_Math" alttext="f_{prompt}(x)" display="inline"><semantics id="A1.S1.p1.1.1.1.1.1.m1.1a"><mrow id="A1.S1.p1.1.1.1.1.1.m1.1.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.cmml"><msub id="A1.S1.p1.1.1.1.1.1.m1.1.2.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.cmml"><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.2.cmml">f</mi><mrow id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.cmml"><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.2.cmml">p</mi><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml">​</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.3" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.3.cmml">r</mi><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1a" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml">​</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.4" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.4.cmml">o</mi><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1b" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml">​</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.5" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.5.cmml">m</mi><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1c" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml">​</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.6" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.6.cmml">p</mi><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1d" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml">​</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.7" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.7.cmml">t</mi></mrow></msub><mo lspace="0em" rspace="0em" id="A1.S1.p1.1.1.1.1.1.m1.1.2.1" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.1.cmml">​</mo><mrow id="A1.S1.p1.1.1.1.1.1.m1.1.2.3.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.cmml"><mo stretchy="false" id="A1.S1.p1.1.1.1.1.1.m1.1.2.3.2.1" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.cmml">(</mo><mi id="A1.S1.p1.1.1.1.1.1.m1.1.1" xref="A1.S1.p1.1.1.1.1.1.m1.1.1.cmml">x</mi><mo stretchy="false" id="A1.S1.p1.1.1.1.1.1.m1.1.2.3.2.2" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S1.p1.1.1.1.1.1.m1.1b"><apply id="A1.S1.p1.1.1.1.1.1.m1.1.2.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2"><times id="A1.S1.p1.1.1.1.1.1.m1.1.2.1.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.1"></times><apply id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2"><csymbol cd="ambiguous" id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.1.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2">subscript</csymbol><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.2.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.2">𝑓</ci><apply id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3"><times id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.1"></times><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.2.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.2">𝑝</ci><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.3.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.3">𝑟</ci><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.4.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.4">𝑜</ci><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.5.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.5">𝑚</ci><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.6.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.6">𝑝</ci><ci id="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.7.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.2.2.3.7">𝑡</ci></apply></apply><ci id="A1.S1.p1.1.1.1.1.1.m1.1.1.cmml" xref="A1.S1.p1.1.1.1.1.1.m1.1.1">𝑥</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S1.p1.1.1.1.1.1.m1.1c">f_{prompt}(x)</annotation></semantics></math> that results in the most effective performance on the downstream task.</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.12" class="ltx_tr">
<td id="A1.S1.p1.1.12.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.12.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.12.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Hadi et al. (<a href="#bib.bib83" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.12.2" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.12.2.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.12.2.1.1" class="ltx_p">the instructions provided to an LLM to make it follow specified rules, automation of processes and to ensure that the output generated is of a specific quality or quantity</span>
</span>
</td>
<td id="A1.S1.p1.1.12.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.12.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.12.3.1.1" class="ltx_p">refers to the designing and wording of prompts given to LLMs so as to get a desired response from them.</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.13" class="ltx_tr">
<td id="A1.S1.p1.1.13.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.13.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.13.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Neagu (<a href="#bib.bib193" title="" class="ltx_ref">2023</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.13.2" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.13.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.13.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.13.3.1.1" class="ltx_p">entails various strate- gies, including explicit instruction, and implicit context [21]. Explicit instruction involves providing explicit guidance or constraints to the model through instructions, examples, or specifications. Implicit context leverages the model’s under- standing of the preceding context to influence its response</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.14" class="ltx_tr">
<td id="A1.S1.p1.1.14.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.14.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.14.1.1.1" class="ltx_p" style="width:56.9pt;"><cite class="ltx_cite ltx_citemacro_cite">Dang et al. (<a href="#bib.bib43" title="" class="ltx_ref">2022</a>)</cite></span>
</span>
</td>
<td id="A1.S1.p1.1.14.2" class="ltx_td ltx_border_r ltx_border_t"></td>
<td id="A1.S1.p1.1.14.3" class="ltx_td ltx_align_justify ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.14.3.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.14.3.1.1" class="ltx_p">the systematic practice of constructing prompts to improve the generated output of a generative model</span>
</span>
</td>
</tr>
<tr id="A1.S1.p1.1.15" class="ltx_tr">
<td id="A1.S1.p1.1.15.1" class="ltx_td ltx_align_justify ltx_align_top ltx_border_l ltx_border_r ltx_border_t">
<span id="A1.S1.p1.1.15.1.1" class="ltx_inline-block ltx_align_top">
<span id="A1.S1.p1.1.15.1.1.1" class="ltx_p" style="width:56.9pt;"><span id="A1.S1.p1.1.15.1.1.1.1" class="ltx_text ltx_caption">Definitions of Prompt and Prompt Engineering from different papers.</span></span>
</span>
</td>
<td id="A1.S1.p1.1.15.2" class="ltx_td ltx_align_justify ltx_border_t"></td>
<td id="A1.S1.p1.1.15.3" class="ltx_td ltx_align_justify ltx_border_t"></td>
</tr>
</table>
</div>
</section>
<section id="A1.S2" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.2 </span>Extended Vocabulary</h3>

<section id="A1.S2.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.2.1 </span>Prompting Terms</h4>

<section id="A1.S2.SS1.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Context Window</h6>

<div id="A1.S2.SS1.SSS0.Px1.p1" class="ltx_para">
<p id="A1.S2.SS1.SSS0.Px1.p1.1" class="ltx_p">The context window is the space of tokens (for LLMs) which the model can process. It has a maximal length (the context length).</p>
</div>
</section>
<section id="A1.S2.SS1.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Priming</h6>

<div id="A1.S2.SS1.SSS0.Px2.p1" class="ltx_para">
<p id="A1.S2.SS1.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Schulhoff (<a href="#bib.bib248" title="" class="ltx_ref">2022</a>)</cite>

refers to giving a model an initial prompt that lays out certain instructions for the rest of a conversation. This priming prompt might contains a role or other instructions on how to interact with the user. Priming can either be done in the system or user prompt (see below).</p>
</div>
</section>
</section>
<section id="A1.S2.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.2.2 </span>Prompt Engineering Terms</h4>

<section id="A1.S2.SS2.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Conversational Prompt Engineering</h6>

<div id="A1.S2.SS2.SSS0.Px1.p1" class="ltx_para">
<p id="A1.S2.SS2.SSS0.Px1.p1.1" class="ltx_p">is Prompt Engineering <span id="A1.S2.SS2.SSS0.Px1.p1.1.1" class="ltx_text ltx_font_italic">in colloquio</span>. That is, during the course of a conversation with a GenAI, a user may ask the GenAI to refine its output. In contrast, prompt engineering is often done by sending the GenAI a completely new prompt rather than continuing a conversation.</p>
</div>
</section>
</section>
<section id="A1.S2.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.2.3 </span>Fine-Tuning Terms</h4>

<section id="A1.S2.SS3.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt-Based Learning</h6>

<div id="A1.S2.SS3.SSS0.Px1.p1" class="ltx_para">
<p id="A1.S2.SS3.SSS0.Px1.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite>, also known as Prompt Learning <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>); Wang et al. (<a href="#bib.bib288" title="" class="ltx_ref">2023d</a>)</cite> refers to the process of using prompting-related techniques. It often is used in the context of fine-tuning, especially fine-tuning prompts. Due to conflicting usage, we do not use this term.</p>
</div>
</section>
<section id="A1.S2.SS3.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prompt Tuning</h6>

<div id="A1.S2.SS3.SSS0.Px2.p1" class="ltx_para">
<p id="A1.S2.SS3.SSS0.Px2.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_cite">Lester et al. (<a href="#bib.bib135" title="" class="ltx_ref">2021</a>)</cite>

refers to directly optimizing the weights of the prompt itself, usually through some form of gradient-based updates. It has also been referred to has Prompt Fine-Tuning. It should <span id="A1.S2.SS3.SSS0.Px2.p1.1.1" class="ltx_text ltx_font_italic">not</span> be used to refer to discrete prompt engineering.</p>
</div>
</section>
</section>
<section id="A1.S2.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.2.4 </span>Orthogonal Prompt Types</h4>

<div id="A1.S2.SS4.p1" class="ltx_para">
<p id="A1.S2.SS4.p1.1" class="ltx_p">We now discuss terminology for high-level ways of classifying prompts.</p>
</div>
<section id="A1.S2.SS4.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.2.4.1 </span>Originator</h5>

<section id="A1.S2.SS4.SSS1.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">User Prompt</h6>

<div id="A1.S2.SS4.SSS1.Px1.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS1.Px1.p1.1" class="ltx_p">This is the type of prompt that comes from the user. This is the most common form of prompting and is how prompts are usually delivered in consumer applications.</p>
</div>
</section>
<section id="A1.S2.SS4.SSS1.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Assistant Prompt</h6>

<div id="A1.S2.SS4.SSS1.Px2.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS1.Px2.p1.1" class="ltx_p">This "prompt" is simply the output of the LLM itself. It can be considered a prompt (or part of one) when it is fed back into the model, for example as part of a conversation history with a user.</p>
</div>
</section>
<section id="A1.S2.SS4.SSS1.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">System Prompt</h6>

<div id="A1.S2.SS4.SSS1.Px3.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS1.Px3.p1.1" class="ltx_p">This prompt is used to give LLMs high level instructions for interacting with users. Not all models have this.</p>
</div>
</section>
</section>
<section id="A1.S2.SS4.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.2.4.2 </span>Hard vs Soft Prompts</h5>

<section id="A1.S2.SS4.SSS2.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Hard (discrete) Prompt</h6>

<div id="A1.S2.SS4.SSS2.Px1.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS2.Px1.p1.1" class="ltx_p">These prompts only contain tokens that directly correspond to words in the LLM vocabulary.</p>
</div>
</section>
<section id="A1.S2.SS4.SSS2.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Soft (continuous) Prompt</h6>

<div id="A1.S2.SS4.SSS2.Px2.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS2.Px2.p1.1" class="ltx_p">These prompts contain tokens that may not correspond to any word in the vocabulary <cite class="ltx_cite ltx_citemacro_cite">Lester et al. (<a href="#bib.bib135" title="" class="ltx_ref">2021</a>); Wang et al. (<a href="#bib.bib287" title="" class="ltx_ref">2023c</a>)</cite>. Soft prompts can be used when fine-tuning is desired, but modifying the weights of the full model is prohibitively expensive. Thus, a frozen model can be used while allowing gradients to flow through the prompt tokens.</p>
</div>
<div id="A1.S2.SS4.SSS2.Px2.p2" class="ltx_para">
<table id="A1.Ex1" class="ltx_equation ltx_eqn_table">

<tbody><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_eqn_cell ltx_align_center"><math id="A1.Ex1.m1.1" class="ltx_Math" alttext="\text{Hard Prompts}\subseteq\text{Soft Prompts}" display="block"><semantics id="A1.Ex1.m1.1a"><mrow id="A1.Ex1.m1.1.1" xref="A1.Ex1.m1.1.1.cmml"><mtext id="A1.Ex1.m1.1.1.2" xref="A1.Ex1.m1.1.1.2a.cmml">Hard Prompts</mtext><mo id="A1.Ex1.m1.1.1.1" xref="A1.Ex1.m1.1.1.1.cmml">⊆</mo><mtext id="A1.Ex1.m1.1.1.3" xref="A1.Ex1.m1.1.1.3a.cmml">Soft Prompts</mtext></mrow><annotation-xml encoding="MathML-Content" id="A1.Ex1.m1.1b"><apply id="A1.Ex1.m1.1.1.cmml" xref="A1.Ex1.m1.1.1"><subset id="A1.Ex1.m1.1.1.1.cmml" xref="A1.Ex1.m1.1.1.1"></subset><ci id="A1.Ex1.m1.1.1.2a.cmml" xref="A1.Ex1.m1.1.1.2"><mtext id="A1.Ex1.m1.1.1.2.cmml" xref="A1.Ex1.m1.1.1.2">Hard Prompts</mtext></ci><ci id="A1.Ex1.m1.1.1.3a.cmml" xref="A1.Ex1.m1.1.1.3"><mtext id="A1.Ex1.m1.1.1.3.cmml" xref="A1.Ex1.m1.1.1.3">Soft Prompts</mtext></ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.Ex1.m1.1c">\text{Hard Prompts}\subseteq\text{Soft Prompts}</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
</tr></tbody>
</table>
</div>
</section>
</section>
<section id="A1.S2.SS4.SSS3" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.2.4.3 </span>Prediction Styles</h5>

<div id="A1.S2.SS4.SSS3.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS3.p1.1" class="ltx_p">In LLMs, a prediction style is the format in which it predicts the next token. There are two common formats for this in prompting research. We do not discuss non-text prediction styles.</p>
</div>
<section id="A1.S2.SS4.SSS3.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Cloze</h6>

<div id="A1.S2.SS4.SSS3.Px1.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS3.Px1.p1.1" class="ltx_p">In Cloze prompts, the token(s) to be predicted are presented as "slots to fill", usually somewhere in the middle of the prompt <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite>. This is usually the case for earlier transformer models such as BERT <cite class="ltx_cite ltx_citemacro_cite">Chu and Lin (<a href="#bib.bib38" title="" class="ltx_ref">2023</a>)</cite>.</p>
</div>
</section>
<section id="A1.S2.SS4.SSS3.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Prefix</h6>

<div id="A1.S2.SS4.SSS3.Px2.p1" class="ltx_para">
<p id="A1.S2.SS4.SSS3.Px2.p1.1" class="ltx_p">In Prefix prompts, the token to be predicted is at the end of the prompt <cite class="ltx_cite ltx_citemacro_cite">Liu et al. (<a href="#bib.bib159" title="" class="ltx_ref">2023b</a>)</cite>. This is usually the case with modern GPT-style models <cite class="ltx_cite ltx_citemacro_cite">Radford et al. (<a href="#bib.bib229" title="" class="ltx_ref">2019b</a>)</cite>.</p>
</div>
</section>
</section>
</section>
</section>
<section id="A1.S3" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.3 </span>Datasheet</h3>

<div id="A1.S3.p1" class="ltx_para">
<p id="A1.S3.p1.1" class="ltx_p">We present a datasheet <cite class="ltx_cite ltx_citemacro_cite">Gebru et al. (<a href="#bib.bib71" title="" class="ltx_ref">2021</a>)</cite> with more information about the associated paper dataset, which is hosted on <a target="_blank" href="https://huggingface.co/datasets/PromptSystematicReview/Prompt_Systematic_Review_Dataset" title="" class="ltx_ref ltx_href">HuggingFace</a>.</p>
</div>
<section id="A1.S3.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.1 </span>Motivation</h4>

<div id="A1.S3.SS1.p1" class="ltx_para">
<p id="A1.S3.SS1.p1.1" class="ltx_p"><span id="A1.S3.SS1.p1.1.1" class="ltx_text ltx_font_bold">For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.</span> 
<br class="ltx_break">This dataset was created to gather existing literature on prompt engineering in order to analyze all current hard prefix prompting techniques.</p>
</div>
<div id="A1.S3.SS1.p2" class="ltx_para">
<p id="A1.S3.SS1.p2.1" class="ltx_p"><span id="A1.S3.SS1.p2.1.1" class="ltx_text ltx_font_bold">Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?</span> 
<br class="ltx_break">This research was associated with the University of Maryland, Learn Prompting, and sponsored by OpenAI, but not created on the behalf of any particular organization.</p>
</div>
<div id="A1.S3.SS1.p3" class="ltx_para">
<p id="A1.S3.SS1.p3.1" class="ltx_p"><span id="A1.S3.SS1.p3.1.1" class="ltx_text ltx_font_bold">Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.</span> 
<br class="ltx_break">OpenAI contributed $10,000 in credits for their API.</p>
</div>
</section>
<section id="A1.S3.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.2 </span>Composition</h4>

<div id="A1.S3.SS2.p1" class="ltx_para">
<p id="A1.S3.SS2.p1.1" class="ltx_p"><span id="A1.S3.SS2.p1.1.1" class="ltx_text ltx_font_bold">What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.</span> 
<br class="ltx_break">The dataset contains 1,565 research papers in PDF format. Any duplicate papers were removed automatically, though some could exist.</p>
</div>
<div id="A1.S3.SS2.p2" class="ltx_para">
<p id="A1.S3.SS2.p2.1" class="ltx_p"><span id="A1.S3.SS2.p2.1.1" class="ltx_text ltx_font_bold">What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.</span> 
<br class="ltx_break">Each data instance is a research paper as a PDF.</p>
</div>
<div id="A1.S3.SS2.p3" class="ltx_para">
<p id="A1.S3.SS2.p3.1" class="ltx_p"><span id="A1.S3.SS2.p3.1.1" class="ltx_text ltx_font_bold">Is there a label or target associated with each instance? If so, please provide a description.</span> 
<br class="ltx_break">No</p>
</div>
<div id="A1.S3.SS2.p4" class="ltx_para">
<p id="A1.S3.SS2.p4.1" class="ltx_p"><span id="A1.S3.SS2.p4.1.1" class="ltx_text ltx_font_bold">Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.</span> 
<br class="ltx_break">No.</p>
</div>
<div id="A1.S3.SS2.p5" class="ltx_para">
<p id="A1.S3.SS2.p5.1" class="ltx_p"><span id="A1.S3.SS2.p5.1.1" class="ltx_text ltx_font_bold">Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.</span> 
<br class="ltx_break">The papers were gathered in a semi-automated process which introduced the possibility of irrelevant papers being collected and relevant papers not being collected. There were manual reviews done for both possible errors to mitigate these errors.</p>
</div>
<div id="A1.S3.SS2.p6" class="ltx_para">
<p id="A1.S3.SS2.p6.1" class="ltx_p"><span id="A1.S3.SS2.p6.1.1" class="ltx_text ltx_font_bold">Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?</span> 
<br class="ltx_break">It is self-contained.</p>
</div>
<div id="A1.S3.SS2.p7" class="ltx_para">
<p id="A1.S3.SS2.p7.1" class="ltx_p"><span id="A1.S3.SS2.p7.1.1" class="ltx_text ltx_font_bold">Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.</span> 
<br class="ltx_break">No.</p>
</div>
<div id="A1.S3.SS2.p8" class="ltx_para">
<p id="A1.S3.SS2.p8.1" class="ltx_p"><span id="A1.S3.SS2.p8.1.1" class="ltx_text ltx_font_bold">Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.</span> 
<br class="ltx_break">The dataset contains some papers on prompt injection. These papers may contain offensive content including racism and sexism.</p>
</div>
</section>
<section id="A1.S3.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.3 </span>Collection Process</h4>

<div id="A1.S3.SS3.p1" class="ltx_para">
<p id="A1.S3.SS3.p1.1" class="ltx_p"><span id="A1.S3.SS3.p1.1.1" class="ltx_text ltx_font_bold">How was the data associated with each instance acquired?</span> 
<br class="ltx_break">The dataset was compiled from Arxiv, Semantic Scholar, and ACL.</p>
</div>
<div id="A1.S3.SS3.p2" class="ltx_para">
<p id="A1.S3.SS3.p2.1" class="ltx_p"><span id="A1.S3.SS3.p2.1.1" class="ltx_text ltx_font_bold">What mechanisms or procedures were used to collect the data?</span> 
<br class="ltx_break">We wrote scripts to automatically query the APIs of Arxiv and Semantic Scholar.</p>
</div>
<div id="A1.S3.SS3.p3" class="ltx_para">
<p id="A1.S3.SS3.p3.1" class="ltx_p"><span id="A1.S3.SS3.p3.1.1" class="ltx_text ltx_font_bold">Over what timeframe was the data collected?</span> 
<br class="ltx_break">The dataset was curated the duration of the research paper, primarily in February of 2024.</p>
</div>
<div id="A1.S3.SS3.p4" class="ltx_para">
<p id="A1.S3.SS3.p4.1" class="ltx_p"><span id="A1.S3.SS3.p4.1.1" class="ltx_text ltx_font_bold">Were any ethical review processes conducted?</span> 
<br class="ltx_break">No.</p>
</div>
</section>
<section id="A1.S3.SS4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.4 </span>Preprocessing/ Cleaning/ Labeling</h4>

<div id="A1.S3.SS4.p1" class="ltx_para">
<p id="A1.S3.SS4.p1.1" class="ltx_p"><span id="A1.S3.SS4.p1.1.1" class="ltx_text ltx_font_bold">Was any preprocessing/cleaning/labeling of the data done?</span> 
<br class="ltx_break">After collecting data from different sources, we removed duplicate papers and did a manual and semi-automated review of papers to ensure they were all relevant.</p>
</div>
<div id="A1.S3.SS4.p2" class="ltx_para">
<p id="A1.S3.SS4.p2.1" class="ltx_p"><span id="A1.S3.SS4.p2.1.1" class="ltx_text ltx_font_bold">Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data?</span> 
<br class="ltx_break">No, we do not anticipate the use of our preprocessed data. However, raw data can be recovered from the links we store.</p>
</div>
<div id="A1.S3.SS4.p3" class="ltx_para">
<p id="A1.S3.SS4.p3.1" class="ltx_p"><span id="A1.S3.SS4.p3.1.1" class="ltx_text ltx_font_bold">Is the software that was used to preprocess/clean/label the data available?</span> 
<br class="ltx_break">It is contained within our code repository on <a target="_blank" href="https://github.com/trigaten/Prompt_Systematic_Review/tree/main/src/prompt_systematic_review/get_papers" title="" class="ltx_ref ltx_href">Github</a>.</p>
</div>
</section>
<section id="A1.S3.SS5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.5 </span>Uses</h4>

<div id="A1.S3.SS5.p1" class="ltx_para">
<p id="A1.S3.SS5.p1.1" class="ltx_p"><span id="A1.S3.SS5.p1.1.1" class="ltx_text ltx_font_bold">Has the dataset been used for any tasks already?</span> 
<br class="ltx_break">No.</p>
</div>
<div id="A1.S3.SS5.p2" class="ltx_para">
<p id="A1.S3.SS5.p2.1" class="ltx_p"><span id="A1.S3.SS5.p2.1.1" class="ltx_text ltx_font_bold">Is there a repository that links to any or all papers or systems that use the dataset?</span> 
<br class="ltx_break"><a target="_blank" href="https://huggingface.co/datasets/PromptSystematicReview/Prompt_Systematic_Review_Dataset" title="" class="ltx_ref ltx_href">Yes</a>.</p>
</div>
<div id="A1.S3.SS5.p3" class="ltx_para">
<p id="A1.S3.SS5.p3.1" class="ltx_p"><span id="A1.S3.SS5.p3.1.1" class="ltx_text ltx_font_bold">Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?</span> 
<br class="ltx_break">All of the papers we collected were written in English. It is possible some papers were not included due to a translation not being available.</p>
</div>
<div id="A1.S3.SS5.p4" class="ltx_para">
<p id="A1.S3.SS5.p4.1" class="ltx_p"><span id="A1.S3.SS5.p4.1.1" class="ltx_text ltx_font_bold">Are there tasks for which the dataset should not be used?</span> 
<br class="ltx_break">No.</p>
</div>
</section>
<section id="A1.S3.SS6" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.6 </span>Distribution</h4>

<div id="A1.S3.SS6.p1" class="ltx_para">
<p id="A1.S3.SS6.p1.1" class="ltx_p"><span id="A1.S3.SS6.p1.1.1" class="ltx_text ltx_font_bold">Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?</span> 
<br class="ltx_break">No.</p>
</div>
</section>
<section id="A1.S3.SS7" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.3.7 </span>Maintenance</h4>

<div id="A1.S3.SS7.p1" class="ltx_para">
<p id="A1.S3.SS7.p1.1" class="ltx_p"><span id="A1.S3.SS7.p1.1.1" class="ltx_text ltx_font_bold">Who will be supporting/hosting/maintaining the dataset?</span> 
<br class="ltx_break">Our team will continue maintenance.</p>
</div>
<div id="A1.S3.SS7.p2" class="ltx_para">
<p id="A1.S3.SS7.p2.1" class="ltx_p"><span id="A1.S3.SS7.p2.1.1" class="ltx_text ltx_font_bold">How can the owner/curator/manager of the dataset be contacted?</span> 
<br class="ltx_break">Please email us at sanderschulhoff@gmail.com</p>
</div>
<div id="A1.S3.SS7.p3" class="ltx_para">
<p id="A1.S3.SS7.p3.1" class="ltx_p"><span id="A1.S3.SS7.p3.1.1" class="ltx_text ltx_font_bold">Is there an erratum?</span> 
<br class="ltx_break">No.</p>
</div>
<div id="A1.S3.SS7.p4" class="ltx_para">
<p id="A1.S3.SS7.p4.1" class="ltx_p"><span id="A1.S3.SS7.p4.1.1" class="ltx_text ltx_font_bold">If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?</span> 
<br class="ltx_break">Yes, anyone is free to use/modify the data.</p>
</div>
</section>
</section>
<section id="A1.S4" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.4 </span>Keywords</h3>

<div id="A1.S4.p1" class="ltx_para">
<p id="A1.S4.p1.1" class="ltx_p">Here are the keywords we used for search.</p>
</div>
<div id="A1.S4.p2" class="ltx_para">
<ul id="A1.S4.I1" class="ltx_itemize">
<li id="A1.S4.I1.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i1.p1" class="ltx_para">
<p id="A1.S4.I1.i1.p1.1" class="ltx_p">jailbreak prompt</p>
</div>
</li>
<li id="A1.S4.I1.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i2.p1" class="ltx_para">
<p id="A1.S4.I1.i2.p1.1" class="ltx_p">prompt an llm</p>
</div>
</li>
<li id="A1.S4.I1.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i3.p1" class="ltx_para">
<p id="A1.S4.I1.i3.p1.1" class="ltx_p">prompt a large language model</p>
</div>
</li>
<li id="A1.S4.I1.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i4.p1" class="ltx_para">
<p id="A1.S4.I1.i4.p1.1" class="ltx_p">prompt injection</p>
</div>
</li>
<li id="A1.S4.I1.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i5.p1" class="ltx_para">
<p id="A1.S4.I1.i5.p1.1" class="ltx_p">prompt optimization</p>
</div>
</li>
<li id="A1.S4.I1.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i6.p1" class="ltx_para">
<p id="A1.S4.I1.i6.p1.1" class="ltx_p">prompt engineering</p>
</div>
</li>
<li id="A1.S4.I1.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i7.p1" class="ltx_para">
<p id="A1.S4.I1.i7.p1.1" class="ltx_p">few-shot learning</p>
</div>
</li>
<li id="A1.S4.I1.i8" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i8.p1" class="ltx_para">
<p id="A1.S4.I1.i8.p1.1" class="ltx_p">few shot learning</p>
</div>
</li>
<li id="A1.S4.I1.i9" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i9.p1" class="ltx_para">
<p id="A1.S4.I1.i9.p1.1" class="ltx_p">prompt-based methods</p>
</div>
</li>
<li id="A1.S4.I1.i10" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i10.p1" class="ltx_para">
<p id="A1.S4.I1.i10.p1.1" class="ltx_p">prompt based methods</p>
</div>
</li>
<li id="A1.S4.I1.i11" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i11.p1" class="ltx_para">
<p id="A1.S4.I1.i11.p1.1" class="ltx_p">prompting-based methods</p>
</div>
</li>
<li id="A1.S4.I1.i12" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i12.p1" class="ltx_para">
<p id="A1.S4.I1.i12.p1.1" class="ltx_p">prompting based methods</p>
</div>
</li>
<li id="A1.S4.I1.i13" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i13.p1" class="ltx_para">
<p id="A1.S4.I1.i13.p1.1" class="ltx_p">few-shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i14" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i14.p1" class="ltx_para">
<p id="A1.S4.I1.i14.p1.1" class="ltx_p">few shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i15" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i15.p1" class="ltx_para">
<p id="A1.S4.I1.i15.p1.1" class="ltx_p">one-shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i16" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i16.p1" class="ltx_para">
<p id="A1.S4.I1.i16.p1.1" class="ltx_p">one shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i17" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i17.p1" class="ltx_para">
<p id="A1.S4.I1.i17.p1.1" class="ltx_p">few-shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i18" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i18.p1" class="ltx_para">
<p id="A1.S4.I1.i18.p1.1" class="ltx_p">few shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i19" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i19.p1" class="ltx_para">
<p id="A1.S4.I1.i19.p1.1" class="ltx_p">one-shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i20" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i20.p1" class="ltx_para">
<p id="A1.S4.I1.i20.p1.1" class="ltx_p">one shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i21" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i21.p1" class="ltx_para">
<p id="A1.S4.I1.i21.p1.1" class="ltx_p">prompting techniques</p>
</div>
</li>
<li id="A1.S4.I1.i22" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i22.p1" class="ltx_para">
<p id="A1.S4.I1.i22.p1.1" class="ltx_p">prompt engineering techniques</p>
</div>
</li>
<li id="A1.S4.I1.i23" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i23.p1" class="ltx_para">
<p id="A1.S4.I1.i23.p1.1" class="ltx_p">llm prompting</p>
</div>
</li>
<li id="A1.S4.I1.i24" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i24.p1" class="ltx_para">
<p id="A1.S4.I1.i24.p1.1" class="ltx_p">large language model prompting</p>
</div>
</li>
<li id="A1.S4.I1.i25" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i25.p1" class="ltx_para">
<p id="A1.S4.I1.i25.p1.1" class="ltx_p">0-shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i26" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i26.p1" class="ltx_para">
<p id="A1.S4.I1.i26.p1.1" class="ltx_p">0 shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i27" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i27.p1" class="ltx_para">
<p id="A1.S4.I1.i27.p1.1" class="ltx_p">zero-shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i28" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i28.p1" class="ltx_para">
<p id="A1.S4.I1.i28.p1.1" class="ltx_p">many-shot prompt</p>
</div>
</li>
<li id="A1.S4.I1.i29" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i29.p1" class="ltx_para">
<p id="A1.S4.I1.i29.p1.1" class="ltx_p">zero-shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i30" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i30.p1" class="ltx_para">
<p id="A1.S4.I1.i30.p1.1" class="ltx_p">many-shot prompting</p>
</div>
</li>
<li id="A1.S4.I1.i31" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i31.p1" class="ltx_para">
<p id="A1.S4.I1.i31.p1.1" class="ltx_p">in-context learning</p>
</div>
</li>
<li id="A1.S4.I1.i32" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i32.p1" class="ltx_para">
<p id="A1.S4.I1.i32.p1.1" class="ltx_p">in context learning</p>
</div>
</li>
<li id="A1.S4.I1.i33" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i33.p1" class="ltx_para">
<p id="A1.S4.I1.i33.p1.1" class="ltx_p">transformer model prompts</p>
</div>
</li>
<li id="A1.S4.I1.i34" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i34.p1" class="ltx_para">
<p id="A1.S4.I1.i34.p1.1" class="ltx_p">prompt-based transfer learning</p>
</div>
</li>
<li id="A1.S4.I1.i35" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i35.p1" class="ltx_para">
<p id="A1.S4.I1.i35.p1.1" class="ltx_p">nlp prompting strategies</p>
</div>
</li>
<li id="A1.S4.I1.i36" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i36.p1" class="ltx_para">
<p id="A1.S4.I1.i36.p1.1" class="ltx_p">llm interpretability via prompts</p>
</div>
</li>
<li id="A1.S4.I1.i37" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i37.p1" class="ltx_para">
<p id="A1.S4.I1.i37.p1.1" class="ltx_p">curriculum learning with prompts</p>
</div>
</li>
<li id="A1.S4.I1.i38" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i38.p1" class="ltx_para">
<p id="A1.S4.I1.i38.p1.1" class="ltx_p">feedback loops in llm prompting</p>
</div>
</li>
<li id="A1.S4.I1.i39" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i39.p1" class="ltx_para">
<p id="A1.S4.I1.i39.p1.1" class="ltx_p">human-in-the-loop prompting</p>
</div>
</li>
<li id="A1.S4.I1.i40" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i40.p1" class="ltx_para">
<p id="A1.S4.I1.i40.p1.1" class="ltx_p">token-efficient prompting</p>
</div>
</li>
<li id="A1.S4.I1.i41" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i41.p1" class="ltx_para">
<p id="A1.S4.I1.i41.p1.1" class="ltx_p">multimodal prompting</p>
</div>
</li>
<li id="A1.S4.I1.i42" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i42.p1" class="ltx_para">
<p id="A1.S4.I1.i42.p1.1" class="ltx_p">instruction prompting</p>
</div>
</li>
<li id="A1.S4.I1.i43" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i43.p1" class="ltx_para">
<p id="A1.S4.I1.i43.p1.1" class="ltx_p">prompt templating</p>
</div>
</li>
<li id="A1.S4.I1.i44" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S4.I1.i44.p1" class="ltx_para">
<p id="A1.S4.I1.i44.p1.1" class="ltx_p">prompt template</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S5" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.5 </span>Evaluation Table</h3>

<figure id="A1.T1" class="ltx_table">
<div id="A1.T1.3" class="ltx_inline-block ltx_align_center ltx_transformed_outer" style="width:500.1pt;height:317.3pt;vertical-align:-0.5pt;"><span class="ltx_transformed_inner" style="transform:translate(-204.6pt,129.6pt) scale(0.55,0.55) ;">
<table id="A1.T1.3.3" class="ltx_tabular ltx_align_middle">
<tr id="A1.T1.3.3.4" class="ltx_tr">
<td id="A1.T1.3.3.4.1" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.1.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">ID</span></td>
<td id="A1.T1.3.3.4.2" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.2.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Model</span></td>
<td id="A1.T1.3.3.4.3" class="ltx_td ltx_align_center ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" colspan="4"><span id="A1.T1.3.3.4.3.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Prompt</span></td>
<td id="A1.T1.3.3.4.4" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.4.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Output Space</span></td>
<td id="A1.T1.3.3.4.5" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.5.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Type</span></td>
<td id="A1.T1.3.3.4.6" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.6.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Res.</span></td>
<td id="A1.T1.3.3.4.7" class="ltx_td ltx_align_left ltx_border_tt" style="padding-left:2.0pt;padding-right:2.0pt;" rowspan="2"><span id="A1.T1.3.3.4.7.1" class="ltx_text ltx_font_bold ltx_font_smallcaps">Batch</span></td>
</tr>
<tr id="A1.T1.3.3.5" class="ltx_tr">
<td id="A1.T1.3.3.5.1" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">Roles</td>
<td id="A1.T1.3.3.5.2" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">CoT</td>
<td id="A1.T1.3.3.5.3" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">Definition</td>
<td id="A1.T1.3.3.5.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">Few-Shot</td>
</tr>
<tr id="A1.T1.3.3.6" class="ltx_tr">
<td id="A1.T1.3.3.6.1" class="ltx_td ltx_align_left ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Kocmi and Federmann <span id="A1.T1.3.3.6.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib126" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.6.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.6.2" class="ltx_td ltx_align_left ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-family</td>
<td id="A1.T1.3.3.6.3" class="ltx_td ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.6.4" class="ltx_td ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.6.5" class="ltx_td ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.6.6" class="ltx_td ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.6.7" class="ltx_td ltx_align_left ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;">DA, sMQM, stars, classes</td>
<td id="A1.T1.3.3.6.8" class="ltx_td ltx_align_left ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.6.9" class="ltx_td ltx_align_left ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.6.10" class="ltx_td ltx_border_t" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.1.1.1" class="ltx_tr">
<td id="A1.T1.1.1.1.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Lu et al. <span id="A1.T1.1.1.1.2.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib170" title="" class="ltx_ref">2023c</a><span id="A1.T1.1.1.1.2.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.1.1.1.3" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Dav3, Turbo, GPT-4</td>
<td id="A1.T1.1.1.1.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.1.1.1.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.1.1.1.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.1.1.1.7" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.1.1.1.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Error Span <math id="A1.T1.1.1.1.1.m1.1" class="ltx_Math" alttext="\rightarrow" display="inline"><semantics id="A1.T1.1.1.1.1.m1.1a"><mo stretchy="false" id="A1.T1.1.1.1.1.m1.1.1" xref="A1.T1.1.1.1.1.m1.1.1.cmml">→</mo><annotation-xml encoding="MathML-Content" id="A1.T1.1.1.1.1.m1.1b"><ci id="A1.T1.1.1.1.1.m1.1.1.cmml" xref="A1.T1.1.1.1.1.m1.1.1">→</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.T1.1.1.1.1.m1.1c">\rightarrow</annotation></semantics></math> Score</td>
<td id="A1.T1.1.1.1.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.1.1.1.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.1.1.1.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.7" class="ltx_tr">
<td id="A1.T1.3.3.7.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Fernandes et al. <span id="A1.T1.3.3.7.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib57" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.7.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.7.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">PaLM</td>
<td id="A1.T1.3.3.7.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.7.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.7.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.7.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.7.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Error Span</td>
<td id="A1.T1.3.3.7.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.7.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.7.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.8" class="ltx_tr">
<td id="A1.T1.3.3.8.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Kocmi and Federmann <span id="A1.T1.3.3.8.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib125" title="" class="ltx_ref">2023a</a><span id="A1.T1.3.3.8.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.8.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4</td>
<td id="A1.T1.3.3.8.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.8.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.8.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.8.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.8.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Error Span</td>
<td id="A1.T1.3.3.8.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.8.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.8.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.9" class="ltx_tr">
<td id="A1.T1.3.3.9.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Araújo and Aguiar <span id="A1.T1.3.3.9.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib5" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.9.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.9.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT</td>
<td id="A1.T1.3.3.9.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.9.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.9.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.9.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.9.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5]</td>
<td id="A1.T1.3.3.9.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.9.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.9.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.10" class="ltx_tr">
<td id="A1.T1.3.3.10.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. <span id="A1.T1.3.3.10.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib286" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.10.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.10.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT</td>
<td id="A1.T1.3.3.10.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.10.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.10.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.10.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.10.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">DA, stars</td>
<td id="A1.T1.3.3.10.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.10.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.10.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.2.2.2" class="ltx_tr">
<td id="A1.T1.2.2.2.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">
<cite class="ltx_cite ltx_citemacro_cite">Liu et al. <span id="A1.T1.2.2.2.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib161" title="" class="ltx_ref">2023d</a><span id="A1.T1.2.2.2.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite><math id="A1.T1.2.2.2.1.m1.1" class="ltx_Math" alttext="\dagger" display="inline"><semantics id="A1.T1.2.2.2.1.m1.1a"><mo id="A1.T1.2.2.2.1.m1.1.1" xref="A1.T1.2.2.2.1.m1.1.1.cmml">†</mo><annotation-xml encoding="MathML-Content" id="A1.T1.2.2.2.1.m1.1b"><ci id="A1.T1.2.2.2.1.m1.1.1.cmml" xref="A1.T1.2.2.2.1.m1.1.1">†</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.T1.2.2.2.1.m1.1c">\dagger</annotation></semantics></math>
</td>
<td id="A1.T1.2.2.2.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-3.5, GPT-4</td>
<td id="A1.T1.2.2.2.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.2.2.2.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.2.2.2.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.2.2.2.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.2.2.2.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-10]</td>
<td id="A1.T1.2.2.2.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.2.2.2.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.2.2.2.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.11" class="ltx_tr">
<td id="A1.T1.3.3.11.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Chan et al. <span id="A1.T1.3.3.11.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib26" title="" class="ltx_ref">2024</a><span id="A1.T1.3.3.11.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.11.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT, GPT-4</td>
<td id="A1.T1.3.3.11.3" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.11.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.11.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.11.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.11.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-10]</td>
<td id="A1.T1.3.3.11.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.11.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.11.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.12" class="ltx_tr">
<td id="A1.T1.3.3.12.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Luo et al. <span id="A1.T1.3.3.12.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib173" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.12.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.12.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT</td>
<td id="A1.T1.3.3.12.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.12.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.12.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.12.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.12.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">yes/no;A/B; Likert [1-10]</td>
<td id="A1.T1.3.3.12.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.12.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.12.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.13" class="ltx_tr">
<td id="A1.T1.3.3.13.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Hada et al. <span id="A1.T1.3.3.13.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib82" title="" class="ltx_ref">2024</a><span id="A1.T1.3.3.13.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.13.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT4-32K</td>
<td id="A1.T1.3.3.13.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.13.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.13.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.13.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.13.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">[0,1,2] or binary</td>
<td id="A1.T1.3.3.13.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.13.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.13.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.14" class="ltx_tr">
<td id="A1.T1.3.3.14.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Fu et al. <span id="A1.T1.3.3.14.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib58" title="" class="ltx_ref">2023a</a><span id="A1.T1.3.3.14.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.14.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT3, OPT, FLAN-T5, GPT2</td>
<td id="A1.T1.3.3.14.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.14.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.14.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.14.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.14.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Probability</td>
<td id="A1.T1.3.3.14.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.14.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.14.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.15" class="ltx_tr">
<td id="A1.T1.3.3.15.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Gao et al. <span id="A1.T1.3.3.15.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib66" title="" class="ltx_ref">2023c</a><span id="A1.T1.3.3.15.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.15.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT</td>
<td id="A1.T1.3.3.15.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.15.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.15.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.15.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.15.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5], Pairwise, Pyramid, 0/1</td>
<td id="A1.T1.3.3.15.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.15.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.15.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.16" class="ltx_tr">
<td id="A1.T1.3.3.16.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. <span id="A1.T1.3.3.16.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib35" title="" class="ltx_ref">2023g</a><span id="A1.T1.3.3.16.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.16.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT</td>
<td id="A1.T1.3.3.16.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.16.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.16.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.16.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.16.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-10]; yes/no; pairwise: A/B/C</td>
<td id="A1.T1.3.3.16.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E &amp; I</td>
<td id="A1.T1.3.3.16.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.16.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.17" class="ltx_tr">
<td id="A1.T1.3.3.17.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">He et al. <span id="A1.T1.3.3.17.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib87" title="" class="ltx_ref">2023a</a><span id="A1.T1.3.3.17.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.17.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4</td>
<td id="A1.T1.3.3.17.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.17.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.17.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.17.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.17.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5]</td>
<td id="A1.T1.3.3.17.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.17.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.17.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.18" class="ltx_tr">
<td id="A1.T1.3.3.18.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Sottana et al. <span id="A1.T1.3.3.18.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib271" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.18.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.18.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4</td>
<td id="A1.T1.3.3.18.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.18.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.18.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.18.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.18.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5]</td>
<td id="A1.T1.3.3.18.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.18.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.18.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.19" class="ltx_tr">
<td id="A1.T1.3.3.19.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Chen et al. <span id="A1.T1.3.3.19.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib31" title="" class="ltx_ref">2023c</a><span id="A1.T1.3.3.19.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.19.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT, Flan-T5</td>
<td id="A1.T1.3.3.19.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.19.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.19.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.19.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.19.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Yes/No</td>
<td id="A1.T1.3.3.19.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.19.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.19.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.20" class="ltx_tr">
<td id="A1.T1.3.3.20.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Zhao et al. <span id="A1.T1.3.3.20.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib346" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.20.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.20.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-3.5, GPT-4</td>
<td id="A1.T1.3.3.20.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.20.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.20.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.20.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.20.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">true/false</td>
<td id="A1.T1.3.3.20.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.20.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.20.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.21" class="ltx_tr">
<td id="A1.T1.3.3.21.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Wu et al. <span id="A1.T1.3.3.21.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib311" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.21.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.21.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-3</td>
<td id="A1.T1.3.3.21.3" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.21.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.21.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.21.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.21.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">pairwise voting</td>
<td id="A1.T1.3.3.21.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.21.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.21.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.22" class="ltx_tr">
<td id="A1.T1.3.3.22.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. <span id="A1.T1.3.3.22.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib294" title="" class="ltx_ref">2023i</a><span id="A1.T1.3.3.22.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.22.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">PaLM 2-IT-L</td>
<td id="A1.T1.3.3.22.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.22.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.22.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.22.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.22.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">A/B</td>
<td id="A1.T1.3.3.22.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.22.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.22.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.23" class="ltx_tr">
<td id="A1.T1.3.3.23.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Jia et al. <span id="A1.T1.3.3.23.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib104" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.23.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.23.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">LLaMa7b</td>
<td id="A1.T1.3.3.23.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.23.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.23.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.23.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.23.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Probability</td>
<td id="A1.T1.3.3.23.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.23.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.23.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.24" class="ltx_tr">
<td id="A1.T1.3.3.24.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Yue et al. <span id="A1.T1.3.3.24.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib335" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.24.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.24.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT, Alpaca, Vicuna, GPT-4</td>
<td id="A1.T1.3.3.24.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.24.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.24.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.24.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.24.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Yes/No</td>
<td id="A1.T1.3.3.24.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.24.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.24.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.25" class="ltx_tr">
<td id="A1.T1.3.3.25.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Li et al. <span id="A1.T1.3.3.25.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib142" title="" class="ltx_ref">2023e</a><span id="A1.T1.3.3.25.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.25.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-3.5, GPT-4, Bard, Vicuna</td>
<td id="A1.T1.3.3.25.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.25.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.25.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.25.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.25.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Pairwise</td>
<td id="A1.T1.3.3.25.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">I</td>
<td id="A1.T1.3.3.25.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.25.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.26" class="ltx_tr">
<td id="A1.T1.3.3.26.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Liu et al. <span id="A1.T1.3.3.26.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib163" title="" class="ltx_ref">2023f</a><span id="A1.T1.3.3.26.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.26.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">ChatGPT, Vicuna, chatGLM, StableLM</td>
<td id="A1.T1.3.3.26.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.26.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.26.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.26.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.26.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">continuous [0-1]</td>
<td id="A1.T1.3.3.26.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.26.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.26.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.27" class="ltx_tr">
<td id="A1.T1.3.3.27.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Bai et al. <span id="A1.T1.3.3.27.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib11" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.27.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.27.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4, Claude, ChatGPT, Bard, Vicuna</td>
<td id="A1.T1.3.3.27.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.27.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.27.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.27.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.27.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5]</td>
<td id="A1.T1.3.3.27.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.27.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.27.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.28" class="ltx_tr">
<td id="A1.T1.3.3.28.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Dubois et al. <span id="A1.T1.3.3.28.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib51" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.28.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.28.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4, ChatGPT, Dav3</td>
<td id="A1.T1.3.3.28.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.28.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.28.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.28.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.28.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">pairwise</td>
<td id="A1.T1.3.3.28.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.28.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.28.10" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
<tr id="A1.T1.3.3.3" class="ltx_tr">
<td id="A1.T1.3.3.3.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">
<cite class="ltx_cite ltx_citemacro_cite">Liu et al. <span id="A1.T1.3.3.3.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib165" title="" class="ltx_ref">2023h</a><span id="A1.T1.3.3.3.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite><math id="A1.T1.3.3.3.1.m1.1" class="ltx_Math" alttext="\dagger" display="inline"><semantics id="A1.T1.3.3.3.1.m1.1a"><mo id="A1.T1.3.3.3.1.m1.1.1" xref="A1.T1.3.3.3.1.m1.1.1.cmml">†</mo><annotation-xml encoding="MathML-Content" id="A1.T1.3.3.3.1.m1.1b"><ci id="A1.T1.3.3.3.1.m1.1.1.cmml" xref="A1.T1.3.3.3.1.m1.1.1">†</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.T1.3.3.3.1.m1.1c">\dagger</annotation></semantics></math>
</td>
<td id="A1.T1.3.3.3.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4-32K</td>
<td id="A1.T1.3.3.3.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.3.4" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.3.5" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.3.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.3.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-5]</td>
<td id="A1.T1.3.3.3.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.3.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.3.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.29" class="ltx_tr">
<td id="A1.T1.3.3.29.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Wang et al. <span id="A1.T1.3.3.29.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib292" title="" class="ltx_ref">2023h</a><span id="A1.T1.3.3.29.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.29.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Turbo, ChatGPT, GPT-4, Vicuna</td>
<td id="A1.T1.3.3.29.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.29.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.29.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.29.6" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.29.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [1-10]</td>
<td id="A1.T1.3.3.29.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.29.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">M</td>
<td id="A1.T1.3.3.29.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.30" class="ltx_tr">
<td id="A1.T1.3.3.30.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Zeng et al. <span id="A1.T1.3.3.30.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib336" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.30.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.30.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">GPT-4, ChatGPT, LLaMA-2-Chat, PaLM2, Falcon</td>
<td id="A1.T1.3.3.30.3" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.30.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.30.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.30.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.30.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Pairwise</td>
<td id="A1.T1.3.3.30.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.30.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.30.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.31" class="ltx_tr">
<td id="A1.T1.3.3.31.1" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Zheng et al. <span id="A1.T1.3.3.31.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib350" title="" class="ltx_ref">2023b</a><span id="A1.T1.3.3.31.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.31.2" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Claude-v1, GPT-3.5, GPT-4</td>
<td id="A1.T1.3.3.31.3" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.31.4" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.31.5" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.31.6" class="ltx_td ltx_align_center" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
<td id="A1.T1.3.3.31.7" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">Pairwise/Likert [1-10]</td>
<td id="A1.T1.3.3.31.8" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.31.9" class="ltx_td ltx_align_left" style="padding-left:2.0pt;padding-right:2.0pt;">S/M</td>
<td id="A1.T1.3.3.31.10" class="ltx_td" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
</tr>
<tr id="A1.T1.3.3.32" class="ltx_tr">
<td id="A1.T1.3.3.32.1" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;"><cite class="ltx_cite ltx_citemacro_cite">Lin and Chen <span id="A1.T1.3.3.32.1.1.1.1.1" class="ltx_text ltx_font_typewriter ltx_font_bold">(</span><a href="#bib.bib155" title="" class="ltx_ref">2023</a><span id="A1.T1.3.3.32.1.2.2.2.1" class="ltx_text ltx_font_typewriter ltx_font_bold">)</span></cite></td>
<td id="A1.T1.3.3.32.2" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;">Claude-v1.3</td>
<td id="A1.T1.3.3.32.3" class="ltx_td ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.32.4" class="ltx_td ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.32.5" class="ltx_td ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.32.6" class="ltx_td ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;"></td>
<td id="A1.T1.3.3.32.7" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;">Likert [0-5], Likert [0-100]</td>
<td id="A1.T1.3.3.32.8" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;">E</td>
<td id="A1.T1.3.3.32.9" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;">S</td>
<td id="A1.T1.3.3.32.10" class="ltx_td ltx_align_left ltx_border_bb" style="padding-left:2.0pt;padding-right:2.0pt;">✓</td>
</tr>
</table>
</span></div>
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_table">Table A.1: </span>Evaluation Paper Summary. E: Explicit (whether the model generates an assessment), I: Implicit (whether an assessment is derived from the model output); Response (Res.) S: Single response, M: Multiple responses; <math id="A1.T1.5.m1.1" class="ltx_Math" alttext="\dagger" display="inline"><semantics id="A1.T1.5.m1.1b"><mo id="A1.T1.5.m1.1.1" xref="A1.T1.5.m1.1.1.cmml">†</mo><annotation-xml encoding="MathML-Content" id="A1.T1.5.m1.1c"><ci id="A1.T1.5.m1.1.1.cmml" xref="A1.T1.5.m1.1.1">†</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.T1.5.m1.1d">\dagger</annotation></semantics></math>: Model generated instruction; </figcaption>
</figure>
</section>
<section id="A1.S6" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.6 </span>Entrapment Prompting Process</h3>

<div id="A1.S6.p1" class="ltx_para">
<p id="A1.S6.p1.1" class="ltx_p">This section contains the thought process of our prompt engineer as he developed the prompt.</p>
</div>
<section id="A1.S6.SS1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.6.1 </span>Exploration</h4>

<div id="A1.S6.SS1.p1" class="ltx_para">
<ul id="A1.S6.I1" class="ltx_itemize">
<li id="A1.S6.I1.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I1.i1.p1" class="ltx_para">
<p id="A1.S6.I1.i1.p1.1" class="ltx_p">First did a bit of dataset exploration, looking at length/label distribution, then chose entrapment to start with.</p>
</div>
</li>
<li id="A1.S6.I1.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I1.i2.p1" class="ltx_para">
<p id="A1.S6.I1.i2.p1.1" class="ltx_p">Checked if gpt-1106-preview understood entrapment, by asking it to define the term WRT SCS. It did not.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S6.SS2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.6.2 </span>Getting a Label</h4>

<div id="A1.S6.SS2.p1" class="ltx_para">
<ul id="A1.S6.I2" class="ltx_itemize">
<li id="A1.S6.I2.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i1.p1" class="ltx_para">
<p id="A1.S6.I2.i1.p1.1" class="ltx_p">Showed it the definition of entrapment in the system prompt and asked it to label a data point, but it responded saying I should seek mental health support.</p>
</div>
</li>
<li id="A1.S6.I2.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i2.p1" class="ltx_para">
<p id="A1.S6.I2.i2.p1.1" class="ltx_p">I put the instructions in the user prompt, but get a similar response.</p>
</div>
</li>
<li id="A1.S6.I2.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i3.p1" class="ltx_para">
<p id="A1.S6.I2.i3.p1.1" class="ltx_p">Appending "Is this entrapment? Yes or no" got it to actually respond with a label.</p>
</div>
</li>
<li id="A1.S6.I2.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i4.p1" class="ltx_para">
<p id="A1.S6.I2.i4.p1.1" class="ltx_p">I gave it a reject example, but it labeled it as accept, and also sometimes gives emotional responses about getting help.</p>
</div>
</li>
<li id="A1.S6.I2.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i5.p1" class="ltx_para">
<p id="A1.S6.I2.i5.p1.1" class="ltx_p">Using 10-shot prompting, it still gives the get help responses.</p>
</div>
</li>
<li id="A1.S6.I2.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i6.p1" class="ltx_para">
<p id="A1.S6.I2.i6.p1.1" class="ltx_p">I tried removing the definition, got the same problem. It seems that it doesn’t like "accept"/"reject" as labels.</p>
</div>
</li>
<li id="A1.S6.I2.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i7.p1" class="ltx_para">
<p id="A1.S6.I2.i7.p1.1" class="ltx_p">I added back the definition, changed labels to entrapment/not entrapment, it often says "not enough info to determine".</p>
</div>
</li>
<li id="A1.S6.I2.i8" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I2.i8.p1" class="ltx_para">
<p id="A1.S6.I2.i8.p1.1" class="ltx_p">I switched to GPT-4-32K, which gave one word responses and correctly predicted a reject and an accept datapoint.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S6.SS3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">
<span class="ltx_tag ltx_tag_subsection">A.6.3 </span>Varying Prompting Techniques</h4>

<div id="A1.S6.SS3.p1" class="ltx_para">
<ul id="A1.S6.I3" class="ltx_itemize">
<li id="A1.S6.I3.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i1.p1" class="ltx_para">
<p id="A1.S6.I3.i1.p1.1" class="ltx_p">Tested 0-shot with context.</p>
</div>
</li>
<li id="A1.S6.I3.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i2.p1" class="ltx_para">
<p id="A1.S6.I3.i2.p1.1" class="ltx_p">10-shot with context, showed improvement.</p>
</div>
</li>
<li id="A1.S6.I3.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i3.p1" class="ltx_para">
<p id="A1.S6.I3.i3.p1.1" class="ltx_p">Taking first failing instance into playground. (12th in train file)</p>
</div>
</li>
<li id="A1.S6.I3.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i4.p1" class="ltx_para">
<p id="A1.S6.I3.i4.p1.1" class="ltx_p">Tried doing ’A: Let’s go step by step and figure this out: ", output "A: Yes"</p>
</div>
</li>
<li id="A1.S6.I3.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i5.p1" class="ltx_para">
<p id="A1.S6.I3.i5.p1.1" class="ltx_p">Did "A: Let’s go step by step and figure this out: ",
OUTPUT:</p>
</div>
<div id="A1.S6.I3.i5.p2" class="ltx_para ltx_noindent">
<svg id="A1.S6.I3.i5.p2.pic1" class="ltx_picture" height="156.09" overflow="visible" version="1.1" width="600"><g transform="translate(0,156.09) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 156.09 L 600 156.09 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 154.12 L 598.03 154.12 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="128.53" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="A1.S6.I3.i5.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="A1.S6.I3.i5.p2.pic1.1.1.1.1.1.1" class="ltx_p">The individual in this post is expressing feelings of hopelessness, loneliness, and despair. They mention a significant loss (their mother) and a subsequent decline in their mental health, turning to alcohol as a coping mechanism. They also mention financial struggles and the possibility of becoming homeless. They express feelings of guilt and shame about their situation and compare themselves negatively to their friends and family. Most concerning is their detailed plan for suicide, involving sleeping pills and alcohol. They also mention considering stepping in front of a car. Given these factors, the label for this post would be "Yes" for expressing feelings of entrapment.</span>
</span></foreignObject></g></g></svg>
</div>
</li>
<li id="A1.S6.I3.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i6.p1" class="ltx_para">
<p id="A1.S6.I3.i6.p1.1" class="ltx_p">"A: Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion.", OUTPUT:</p>
</div>
<div id="A1.S6.I3.i6.p2" class="ltx_para ltx_noindent">
<svg id="A1.S6.I3.i6.p2.pic1" class="ltx_picture" height="56.46" overflow="visible" version="1.1" width="600"><g transform="translate(0,56.46) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 56.46 L 600 56.46 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 54.49 L 598.03 54.49 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="28.9" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="A1.S6.I3.i6.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="A1.S6.I3.i6.p2.pic1.1.1.1.1.1.1" class="ltx_p">Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion.</span>
</span></foreignObject></g></g></svg>
</div>
</li>
<li id="A1.S6.I3.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i7.p1" class="ltx_para">
<p id="A1.S6.I3.i7.p1.1" class="ltx_p">Began discussing with the AI "they don’t ever say they feel trapped or that there is no way out, how did u get that idea?", OUTPUT:</p>
</div>
<div id="A1.S6.I3.i7.p2" class="ltx_para ltx_noindent">
<svg id="A1.S6.I3.i7.p2.pic1" class="ltx_picture" height="139.48" overflow="visible" version="1.1" width="600"><g transform="translate(0,139.48) matrix(1 0 0 -1 0 0)" fill="#000000" stroke="#000000" stroke-width="0.4pt"><g fill="#404040" fill-opacity="1.0"><path d="M 0 0 L 0 139.48 L 600 139.48 L 600 0 Z" style="stroke:none"></path></g><g fill="#D9ECEC" fill-opacity="1.0"><path d="M 1.97 1.97 L 1.97 137.52 L 598.03 137.52 L 598.03 1.97 Z" style="stroke:none"></path></g><g fill-opacity="1.0" transform="matrix(1.0 0.0 0.0 1.0 21.65 13.78)"><foreignObject width="556.69" height="111.93" transform="matrix(1 0 0 -1 0 16.6)" overflow="visible" color="#000000">
<span id="A1.S6.I3.i7.p2.pic1.1.1.1.1.1" class="ltx_inline-block ltx_minipage ltx_align_bottom" style="width:402.3pt;">
<span id="A1.S6.I3.i7.p2.pic1.1.1.1.1.1.1" class="ltx_p">The individual doesn’t explicitly state that they feel trapped or that there is no way out, but their narrative suggests a sense of entrapment. They mention that they are facing eviction, struggling with debt, and dealing with a decrease in income. They also express a sense of hopelessness and despair, and contemplate suicide as a potential solution to their problems. These elements suggest a feeling of being trapped in their current circumstances with no apparent way out. However, it’s important to note that this interpretation is based on the information provided in the post and the individual’s expressed feelings and thoughts.</span>
</span></foreignObject></g></g></svg>
</div>
</li>
<li id="A1.S6.I3.i8" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i8.p1" class="ltx_para">
<p id="A1.S6.I3.i8.p1.1" class="ltx_p">Asked it to help reformulate instructions to make explicitness more clearly needed, still predicts entrapment.</p>
</div>
</li>
<li id="A1.S6.I3.i9" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i9.p1" class="ltx_para">
<p id="A1.S6.I3.i9.p1.1" class="ltx_p">Added "ENTRAPMENT MUST BE EXPLICIT, NOT IMPLICIT." to instruction at top after definitions (no CoT inducer)</p>
</div>
</li>
<li id="A1.S6.I3.i10" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i10.p1" class="ltx_para">
<p id="A1.S6.I3.i10.p1.1" class="ltx_p">Also tried with previous CoT inducer, still says entrapment in a hard to parse out way.</p>
</div>
</li>
<li id="A1.S6.I3.i11" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i11.p1" class="ltx_para">
<p id="A1.S6.I3.i11.p1.1" class="ltx_p">Added "If the person does not explicitly say they feel trapped, this doesn’t count as entrapment." (no CoT), still entrapment.</p>
</div>
</li>
<li id="A1.S6.I3.i12" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i12.p1" class="ltx_para">
<p id="A1.S6.I3.i12.p1.1" class="ltx_p">Talking to AI, it thinks "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" is feeling trapped/stuck.</p>
</div>
</li>
<li id="A1.S6.I3.i13" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i13.p1" class="ltx_para">
<p id="A1.S6.I3.i13.p1.1" class="ltx_p">Tried "Before stating a label, write out reasoning using the grid above about why this may/may not be entrapment:", still thinks entrapment.</p>
</div>
</li>
<li id="A1.S6.I3.i14" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i14.p1" class="ltx_para">
<p id="A1.S6.I3.i14.p1.1" class="ltx_p">paste in email: [REDACTED] above definition of entrapment</p>
</div>
</li>
<li id="A1.S6.I3.i15" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i15.p1" class="ltx_para">
<p id="A1.S6.I3.i15.p1.1" class="ltx_p">remove shots, says "Entrapment"</p>
</div>
</li>
<li id="A1.S6.I3.i16" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i16.p1" class="ltx_para">
<p id="A1.S6.I3.i16.p1.1" class="ltx_p">add this after def: IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped., says "Yes"</p>
</div>
</li>
<li id="A1.S6.I3.i17" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i17.p1" class="ltx_para">
<p id="A1.S6.I3.i17.p1.1" class="ltx_p">In the prompt, gave it CoT reasoning. (18.txt), and tried with the next wrongly labeled one (15), (full prompt, 19.txt)</p>
</div>
</li>
<li id="A1.S6.I3.i18" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i18.p1" class="ltx_para">
<p id="A1.S6.I3.i18.p1.1" class="ltx_p">Tested this on everything except first 20, did pretty well</p>
</div>
</li>
<li id="A1.S6.I3.i19" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i19.p1" class="ltx_para">
<p id="A1.S6.I3.i19.p1.1" class="ltx_p">Tried removing email, performance dropped of a cliff</p>
</div>
</li>
<li id="A1.S6.I3.i20" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i20.p1" class="ltx_para">
<p id="A1.S6.I3.i20.p1.1" class="ltx_p">At this point, I am thinking that giving examples with reasoning helps (obviously)</p>
</div>
</li>
<li id="A1.S6.I3.i21" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I3.i21.p1" class="ltx_para">
<p id="A1.S6.I3.i21.p1.1" class="ltx_p">Tried to add 10 shots in for free, before the last one with reasoning, bad results</p>
</div>
</li>
</ul>
</div>
<section id="A1.S6.SS3.SSS1" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.6.3.1 </span>AutoCoT</h5>

<div id="A1.S6.SS3.SSS1.p1" class="ltx_para">
<ul id="A1.S6.I4" class="ltx_itemize">
<li id="A1.S6.I4.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i1.p1" class="ltx_para">
<p id="A1.S6.I4.i1.p1.1" class="ltx_p">Develop dataset using this prompt (22.txt). Then ask it "Why?". If it disagrees, I say "It is actually not entrapment, please explain why." (accidentally duplicated email 23.txt)</p>
</div>
</li>
<li id="A1.S6.I4.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i2.p1" class="ltx_para">
<p id="A1.S6.I4.i2.p1.1" class="ltx_p">Just for fun, tried 0 shot full context (had to adjust verbalizer)</p>
</div>
</li>
<li id="A1.S6.I4.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i3.p1" class="ltx_para">
<p id="A1.S6.I4.i3.p1.1" class="ltx_p">tried this with special verbalizer which catches "This post does not meet the criteria for Entrapment."</p>
</div>
</li>
<li id="A1.S6.I4.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i4.p1" class="ltx_para">
<p id="A1.S6.I4.i4.p1.1" class="ltx_p">Tested my generated data, beat 0.5 F1</p>
</div>
</li>
<li id="A1.S6.I4.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i5.p1" class="ltx_para">
<p id="A1.S6.I4.i5.p1.1" class="ltx_p">Doing 10 more exemplars w autocot. Sometimes responds immediately with reasoning like "This post does not meet the criteria for Entrapment as the individual does not explicitly express feelings of being trapped or hopeless.", so just use that if so. Sometimes get refusal "I’m really sorry to hear that you’re feeling this way, but I’m unable to provide the help that you need. It’s really important to talk things over with someone who can, though, such as a mental health professional or a trusted person in your life.", just ask "Explain why it is not entrapment." after if so.</p>
</div>
</li>
<li id="A1.S6.I4.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i6.p1" class="ltx_para">
<p id="A1.S6.I4.i6.p1.1" class="ltx_p">performance didnt really improve, realized about 11% are getting -1, meaning not extracted properly. Retrying with full words "Question" instead of Q, also for reasoning and answer.</p>
</div>
</li>
<li id="A1.S6.I4.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I4.i7.p1" class="ltx_para">
<p id="A1.S6.I4.i7.p1.1" class="ltx_p">this led to higher inability to parse, at about 16%.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S6.SS3.SSS2" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.6.3.2 </span>Developing Answer Extraction</h5>

<div id="A1.S6.SS3.SSS2.p1" class="ltx_para">
<ul id="A1.S6.I5" class="ltx_itemize">
<li id="A1.S6.I5.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I5.i1.p1" class="ltx_para">
<p id="A1.S6.I5.i1.p1.1" class="ltx_p">put first failing to parse one in (22), and developed a prompt for it.</p>
</div>
</li>
<li id="A1.S6.I5.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I5.i2.p1" class="ltx_para">
<p id="A1.S6.I5.i2.p1.1" class="ltx_p">did worse: (0.42857142857142855, 0.5051546391752577, 0.8571428571428571, 0.2857142857142857)</p>
</div>
</li>
<li id="A1.S6.I5.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I5.i3.p1" class="ltx_para">
<p id="A1.S6.I5.i3.p1.1" class="ltx_p">only using extracted label if have -1 helps slightly to (0.48, 0.61, 0.8571428571428571, 0.3333333333333333)</p>
</div>
</li>
<li id="A1.S6.I5.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I5.i4.p1" class="ltx_para">
<p id="A1.S6.I5.i4.p1.1" class="ltx_p">going back to best performing prompt–10 QRA shot, and performing extraction with any -1s, doesnt help other than gently boosting accuracy, perhaps when it doesnt answer</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S6.SS3.SSS3" class="ltx_subsubsection">
<h5 class="ltx_title ltx_title_subsubsection">
<span class="ltx_tag ltx_tag_subsubsection">A.6.3.3 </span>Iterating on Email</h5>

<div id="A1.S6.SS3.SSS3.p1" class="ltx_para">
<ul id="A1.S6.I6" class="ltx_itemize">
<li id="A1.S6.I6.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I6.i1.p1" class="ltx_para">
<p id="A1.S6.I6.i1.p1.1" class="ltx_p">tried best perf, with no email</p>
</div>
</li>
<li id="A1.S6.I6.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I6.i2.p1" class="ltx_para">
<p id="A1.S6.I6.i2.p1.1" class="ltx_p">tried with deduped email, worse results</p>
</div>
</li>
<li id="A1.S6.I6.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I6.i3.p1" class="ltx_para">
<p id="A1.S6.I6.i3.p1.1" class="ltx_p">noticed that ones its unsure about often contained 1 labels that should be 0, so trying to "recover" these doesnt help</p>
</div>
</li>
<li id="A1.S6.I6.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I6.i4.p1" class="ltx_para">
<p id="A1.S6.I6.i4.p1.1" class="ltx_p">try moving around exemplar order, performing extraction, didnt help</p>
</div>
</li>
<li id="A1.S6.I6.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S6.I6.i5.p1" class="ltx_para">
<p id="A1.S6.I6.i5.p1.1" class="ltx_p">triplicated email, didnt help</p>
</div>
</li>
</ul>
</div>
</section>
</section>
</section>
<section id="A1.S7" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.7 </span>Formally Defining a Prompt</h3>

<div id="A1.S7.p1" class="ltx_para">
<p id="A1.S7.p1.1" class="ltx_p">"Prompt" is a widely used term, but uses and definitions differ widely across research. As a result, it is difficult to create a formal, mathematical definition for a prompt. In this section, we outline some formalisms for prompt engineering.</p>
</div>
<section id="A1.S7.SS0.SSS0.Px1" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">As a conditioning Mechanism.</h6>

<div id="A1.S7.SS0.SSS0.Px1.p1" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px1.p1.2" class="ltx_p"><cite class="ltx_cite ltx_citemacro_citet">Qiao et al. (<a href="#bib.bib224" title="" class="ltx_ref">2022</a>)</cite> present the following definition, which involves the prompt <math id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1" class="ltx_Math" alttext="\mathcal{T}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px1.p1.1.m1.1.1.cmml">𝒯</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1b"><ci id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p1.1.m1.1.1">𝒯</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px1.p1.1.m1.1c">\mathcal{T}</annotation></semantics></math> and a question <math id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1" class="ltx_Math" alttext="\mathcal{Q}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px1.p1.2.m2.1.1.cmml">𝒬</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1b"><ci id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p1.2.m2.1.1">𝒬</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px1.p1.2.m2.1c">\mathcal{Q}</annotation></semantics></math> as conditioning mechanisms on predicting the next token. Note that they appear to use <cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>’s original definition of prompt, which refers to the non-question part of the prompt (e.g. few-shot exemplars, instructions).</p>
</div>
<div id="A1.S7.SS0.SSS0.Px1.p2" class="ltx_para">
<table id="A1.S9.EGx1" class="ltx_equationgroup ltx_eqn_align ltx_eqn_table">

<tbody id="A1.E1"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E1.m1.7" class="ltx_Math" alttext="\displaystyle p(\mathcal{A}\mid\mathcal{T},\mathcal{Q})=\prod_{i=1}^{|\mathcal{A}|}p_{\rm LM}\left(a_{i}\mid\mathcal{T},\mathcal{Q},a_{1:i-1}\right)" display="inline"><semantics id="A1.E1.m1.7a"><mrow id="A1.E1.m1.7.7" xref="A1.E1.m1.7.7.cmml"><mrow id="A1.E1.m1.6.6.1" xref="A1.E1.m1.6.6.1.cmml"><mi id="A1.E1.m1.6.6.1.3" xref="A1.E1.m1.6.6.1.3.cmml">p</mi><mo lspace="0em" rspace="0em" id="A1.E1.m1.6.6.1.2" xref="A1.E1.m1.6.6.1.2.cmml">​</mo><mrow id="A1.E1.m1.6.6.1.1.1" xref="A1.E1.m1.6.6.1.1.1.1.cmml"><mo stretchy="false" id="A1.E1.m1.6.6.1.1.1.2" xref="A1.E1.m1.6.6.1.1.1.1.cmml">(</mo><mrow id="A1.E1.m1.6.6.1.1.1.1" xref="A1.E1.m1.6.6.1.1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E1.m1.6.6.1.1.1.1.2" xref="A1.E1.m1.6.6.1.1.1.1.2.cmml">𝒜</mi><mo id="A1.E1.m1.6.6.1.1.1.1.1" xref="A1.E1.m1.6.6.1.1.1.1.1.cmml">∣</mo><mrow id="A1.E1.m1.6.6.1.1.1.1.3.2" 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id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3"><ci id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.1.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.1">:</ci><cn type="integer" id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.2.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.2">1</cn><apply id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3"><minus id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.1.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.1"></minus><ci id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.2.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.2">𝑖</ci><cn type="integer" id="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.3.cmml" xref="A1.E1.m1.7.7.2.1.1.1.1.1.1.1.3.3.3">1</cn></apply></apply></apply></list></apply></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E1.m1.7c">\displaystyle p(\mathcal{A}\mid\mathcal{T},\mathcal{Q})=\prod_{i=1}^{|\mathcal{A}|}p_{\rm LM}\left(a_{i}\mid\mathcal{T},\mathcal{Q},a_{1:i-1}\right)</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.1)</span></td>
</tr></tbody>
</table>
</div>
<div id="A1.S7.SS0.SSS0.Px1.p3" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px1.p3.3" class="ltx_p">Here, the prompt and question condition the pre-trained LLM <math id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1" class="ltx_Math" alttext="p_{\rm LM}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1a"><msub id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.cmml"><mi id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.2" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.2.cmml">p</mi><mi id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.3" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.3.cmml">LM</mi></msub><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1b"><apply id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.2">𝑝</ci><ci id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.1.m1.1.1.3">LM</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px1.p3.1.m1.1c">p_{\rm LM}</annotation></semantics></math>. The <math id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1" class="ltx_Math" alttext="a_{1:i-1}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1a"><msub id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.cmml"><mi id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.2" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.2.cmml">a</mi><mrow id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.cmml"><mn id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.2" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.2.cmml">1</mn><mo lspace="0.278em" rspace="0.278em" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.1" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.1.cmml">:</mo><mrow id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.cmml"><mi id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.2" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.2.cmml">i</mi><mo id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.1" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.1.cmml">−</mo><mn id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.3" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.3.cmml">1</mn></mrow></mrow></msub><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1b"><apply id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.2">𝑎</ci><apply id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3"><ci id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.1">:</ci><cn type="integer" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.2.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.2">1</cn><apply id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3"><minus id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.1"></minus><ci id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.2.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.2">𝑖</ci><cn type="integer" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.3.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.2.m2.1.1.3.3.3">1</cn></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px1.p3.2.m2.1c">a_{1:i-1}</annotation></semantics></math> are previously generated answer tokens and <math id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1" class="ltx_Math" alttext="\mathcal{A}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1.1" xref="A1.S7.SS0.SSS0.Px1.p3.3.m3.1.1.cmml">𝒜</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1b"><ci id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1.1.cmml" xref="A1.S7.SS0.SSS0.Px1.p3.3.m3.1.1">𝒜</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px1.p3.3.m3.1c">\mathcal{A}</annotation></semantics></math> a complete answer.</p>
</div>
</section>
<section id="A1.S7.SS0.SSS0.Px2" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Templating.</h6>

<div id="A1.S7.SS0.SSS0.Px2.p1" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px2.p1.1" class="ltx_p">The above formalization does not include the notion of maximizing a scoring or utility function (e.g. accuracy on a dataset), which prompts are often designed to do. Additionally, prompt engineers often seek to design prompt template rather than prompts. Here, we reformulate <a href="#A1.E1" title="In As a conditioning Mechanism. ‣ A.7 Formally Defining a Prompt ‣ Appendix A Appendices ‣ The Prompt Report: A Systematic Survey of Prompting Techniques" class="ltx_ref"><span class="ltx_text ltx_ref_tag">eq.</span> <span class="ltx_text ltx_ref_tag">A.1</span></a> to include the prompt template:</p>
</div>
<div id="A1.S7.SS0.SSS0.Px2.p2" class="ltx_para">
<table id="A1.S9.EGx2" class="ltx_equationgroup ltx_eqn_align ltx_eqn_table">

<tbody id="A1.E2"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
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xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2"><csymbol cd="ambiguous" id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.1.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2">subscript</csymbol><ci id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.2.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.2">𝑎</ci><apply id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3"><ci id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.1.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.1">:</ci><cn type="integer" id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.2.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.2">1</cn><apply id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3"><minus id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.1.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.1"></minus><ci id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.2.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.2">𝑖</ci><cn type="integer" id="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.3.cmml" xref="A1.E2.m1.3.3.2.1.1.1.1.2.2.2.3.3.3">1</cn></apply></apply></apply></list></apply></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E2.m1.3c">\displaystyle p(\mathcal{A}\mid\mathcal{T}(x^{*}))=\prod_{i=1}^{|\mathcal{A}|}p_{\rm LM}\left(a_{i}\mid\mathcal{T}(x^{*}),a_{1:i-1}\right)</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.2)</span></td>
</tr></tbody>
</table>
</div>
<div id="A1.S7.SS0.SSS0.Px2.p3" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px2.p3.5" class="ltx_p">We replace <math id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1" class="ltx_Math" alttext="\mathcal{Q}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.1.m1.1.1.cmml">𝒬</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1b"><ci id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.1.m1.1.1">𝒬</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px2.p3.1.m1.1c">\mathcal{Q}</annotation></semantics></math> with <math id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1" class="ltx_Math" alttext="x^{*}\in\mathcal{D}_{\text{eval}}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1a"><mrow id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.cmml"><msup id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.cmml"><mi id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.2" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.2.cmml">x</mi><mo id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.3" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.3.cmml">∗</mo></msup><mo id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.1.cmml">∈</mo><msub id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.2" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.2.cmml">𝒟</mi><mtext id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3a.cmml">eval</mtext></msub></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1b"><apply id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1"><in id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.1"></in><apply id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2">superscript</csymbol><ci id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.2">𝑥</ci><times id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.3.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.2.3"></times></apply><apply id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.2">𝒟</ci><ci id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3a.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3"><mtext mathsize="70%" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.2.m2.1.1.3.3">eval</mtext></ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px2.p3.2.m2.1c">x^{*}\in\mathcal{D}_{\text{eval}}</annotation></semantics></math>, an item from a dataset (e.g., evaluation data). Additionally, we replace <math id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1" class="ltx_Math" alttext="\mathcal{Q}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.3.m3.1.1.cmml">𝒬</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1b"><ci id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.3.m3.1.1">𝒬</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px2.p3.3.m3.1c">\mathcal{Q}</annotation></semantics></math> on the right side with <math id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1" class="ltx_Math" alttext="\mathcal{T}(x)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1a"><mrow id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.2" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.2.cmml">𝒯</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.1" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.3.2" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.cmml">(</mo><mi id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.1.cmml">x</mi><mo stretchy="false" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1b"><apply id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2"><times id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.2.2">𝒯</ci><ci id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.4.m4.1.1">𝑥</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px2.p3.4.m4.1c">\mathcal{T}(x)</annotation></semantics></math>. <math id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1" class="ltx_Math" alttext="\mathcal{T}(\cdot)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1a"><mrow id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.2" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.2.cmml">𝒯</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.1" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.3.2" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.cmml">(</mo><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.1" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.1.cmml">⋅</mo><mo stretchy="false" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1b"><apply id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2"><times id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.2.2">𝒯</ci><ci id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.1.cmml" xref="A1.S7.SS0.SSS0.Px2.p3.5.m5.1.1">⋅</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px2.p3.5.m5.1c">\mathcal{T}(\cdot)</annotation></semantics></math> is a prompt template: a function that accepts some item as input then returns a prompt that is used to condition the model.</p>
</div>
</section>
<section id="A1.S7.SS0.SSS0.Px3" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Few-Shot Prompting.</h6>

<div id="A1.S7.SS0.SSS0.Px3.p1" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px3.p1.2" class="ltx_p">Often, an important part of the prompting process is the use of few-shot exemplars. <math id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1" class="ltx_Math" alttext="\mathcal{D}_{train}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1a"><msub id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.2" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.2.cmml">𝒟</mi><mrow id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.cmml"><mi id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.2" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.2.cmml">t</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1.cmml">​</mo><mi id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.3" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.3.cmml">r</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1a" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1.cmml">​</mo><mi id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.4" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.4.cmml">a</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1b" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1.cmml">​</mo><mi id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.5" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.5.cmml">i</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1c" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1.cmml">​</mo><mi id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.6" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.6.cmml">n</mi></mrow></msub><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1b"><apply id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.2">𝒟</ci><apply id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3"><times id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.1"></times><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.2">𝑡</ci><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.3">𝑟</ci><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.4.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.4">𝑎</ci><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.5.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.5">𝑖</ci><ci id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.6.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.1.m1.1.1.3.6">𝑛</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px3.p1.1.m1.1c">\mathcal{D}_{train}</annotation></semantics></math> is training data (used to build the prompt) and <math id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1" class="ltx_Math" alttext="\mathcal{X}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px3.p1.2.m2.1.1.cmml">𝒳</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1b"><ci id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p1.2.m2.1.1">𝒳</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px3.p1.2.m2.1c">\mathcal{X}</annotation></semantics></math> is a test set for evaluation.</p>
</div>
<div id="A1.S7.SS0.SSS0.Px3.p2" class="ltx_para">
<table id="A1.S9.EGx3" class="ltx_equationgroup ltx_eqn_align ltx_eqn_table">

<tbody id="A1.E3"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E3.m1.1" class="ltx_Math" alttext="\displaystyle\mathcal{D}_{\text{train}}" display="inline"><semantics id="A1.E3.m1.1a"><msub id="A1.E3.m1.1.1" xref="A1.E3.m1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E3.m1.1.1.2" xref="A1.E3.m1.1.1.2.cmml">𝒟</mi><mtext id="A1.E3.m1.1.1.3" xref="A1.E3.m1.1.1.3a.cmml">train</mtext></msub><annotation-xml encoding="MathML-Content" id="A1.E3.m1.1b"><apply id="A1.E3.m1.1.1.cmml" xref="A1.E3.m1.1.1"><csymbol cd="ambiguous" id="A1.E3.m1.1.1.1.cmml" xref="A1.E3.m1.1.1">subscript</csymbol><ci id="A1.E3.m1.1.1.2.cmml" xref="A1.E3.m1.1.1.2">𝒟</ci><ci id="A1.E3.m1.1.1.3a.cmml" xref="A1.E3.m1.1.1.3"><mtext mathsize="70%" id="A1.E3.m1.1.1.3.cmml" xref="A1.E3.m1.1.1.3">train</mtext></ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E3.m1.1c">\displaystyle\mathcal{D}_{\text{train}}</annotation></semantics></math></td>
<td class="ltx_td ltx_align_left ltx_eqn_cell"><math id="A1.E3.m2.4" class="ltx_Math" alttext="\displaystyle=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{n},y_{n})\}" display="inline"><semantics id="A1.E3.m2.4a"><mrow id="A1.E3.m2.4.4" xref="A1.E3.m2.4.4.cmml"><mi id="A1.E3.m2.4.4.5" xref="A1.E3.m2.4.4.5.cmml"></mi><mo id="A1.E3.m2.4.4.4" xref="A1.E3.m2.4.4.4.cmml">=</mo><mrow id="A1.E3.m2.4.4.3.3" xref="A1.E3.m2.4.4.3.4.cmml"><mo stretchy="false" id="A1.E3.m2.4.4.3.3.4" xref="A1.E3.m2.4.4.3.4.cmml">{</mo><mrow id="A1.E3.m2.2.2.1.1.1.2" xref="A1.E3.m2.2.2.1.1.1.3.cmml"><mo stretchy="false" id="A1.E3.m2.2.2.1.1.1.2.3" xref="A1.E3.m2.2.2.1.1.1.3.cmml">(</mo><msub id="A1.E3.m2.2.2.1.1.1.1.1" xref="A1.E3.m2.2.2.1.1.1.1.1.cmml"><mi id="A1.E3.m2.2.2.1.1.1.1.1.2" xref="A1.E3.m2.2.2.1.1.1.1.1.2.cmml">x</mi><mn id="A1.E3.m2.2.2.1.1.1.1.1.3" xref="A1.E3.m2.2.2.1.1.1.1.1.3.cmml">1</mn></msub><mo id="A1.E3.m2.2.2.1.1.1.2.4" xref="A1.E3.m2.2.2.1.1.1.3.cmml">,</mo><msub id="A1.E3.m2.2.2.1.1.1.2.2" xref="A1.E3.m2.2.2.1.1.1.2.2.cmml"><mi id="A1.E3.m2.2.2.1.1.1.2.2.2" xref="A1.E3.m2.2.2.1.1.1.2.2.2.cmml">y</mi><mn id="A1.E3.m2.2.2.1.1.1.2.2.3" xref="A1.E3.m2.2.2.1.1.1.2.2.3.cmml">1</mn></msub><mo stretchy="false" id="A1.E3.m2.2.2.1.1.1.2.5" xref="A1.E3.m2.2.2.1.1.1.3.cmml">)</mo></mrow><mo id="A1.E3.m2.4.4.3.3.5" xref="A1.E3.m2.4.4.3.4.cmml">,</mo><mrow id="A1.E3.m2.3.3.2.2.2.2" xref="A1.E3.m2.3.3.2.2.2.3.cmml"><mo stretchy="false" id="A1.E3.m2.3.3.2.2.2.2.3" xref="A1.E3.m2.3.3.2.2.2.3.cmml">(</mo><msub id="A1.E3.m2.3.3.2.2.2.1.1" xref="A1.E3.m2.3.3.2.2.2.1.1.cmml"><mi id="A1.E3.m2.3.3.2.2.2.1.1.2" xref="A1.E3.m2.3.3.2.2.2.1.1.2.cmml">x</mi><mn id="A1.E3.m2.3.3.2.2.2.1.1.3" xref="A1.E3.m2.3.3.2.2.2.1.1.3.cmml">2</mn></msub><mo id="A1.E3.m2.3.3.2.2.2.2.4" xref="A1.E3.m2.3.3.2.2.2.3.cmml">,</mo><msub id="A1.E3.m2.3.3.2.2.2.2.2" xref="A1.E3.m2.3.3.2.2.2.2.2.cmml"><mi id="A1.E3.m2.3.3.2.2.2.2.2.2" xref="A1.E3.m2.3.3.2.2.2.2.2.2.cmml">y</mi><mn id="A1.E3.m2.3.3.2.2.2.2.2.3" xref="A1.E3.m2.3.3.2.2.2.2.2.3.cmml">2</mn></msub><mo stretchy="false" id="A1.E3.m2.3.3.2.2.2.2.5" xref="A1.E3.m2.3.3.2.2.2.3.cmml">)</mo></mrow><mo id="A1.E3.m2.4.4.3.3.6" xref="A1.E3.m2.4.4.3.4.cmml">,</mo><mi mathvariant="normal" id="A1.E3.m2.1.1" xref="A1.E3.m2.1.1.cmml">…</mi><mo id="A1.E3.m2.4.4.3.3.7" xref="A1.E3.m2.4.4.3.4.cmml">,</mo><mrow id="A1.E3.m2.4.4.3.3.3.2" xref="A1.E3.m2.4.4.3.3.3.3.cmml"><mo stretchy="false" id="A1.E3.m2.4.4.3.3.3.2.3" xref="A1.E3.m2.4.4.3.3.3.3.cmml">(</mo><msub id="A1.E3.m2.4.4.3.3.3.1.1" xref="A1.E3.m2.4.4.3.3.3.1.1.cmml"><mi id="A1.E3.m2.4.4.3.3.3.1.1.2" xref="A1.E3.m2.4.4.3.3.3.1.1.2.cmml">x</mi><mi id="A1.E3.m2.4.4.3.3.3.1.1.3" xref="A1.E3.m2.4.4.3.3.3.1.1.3.cmml">n</mi></msub><mo id="A1.E3.m2.4.4.3.3.3.2.4" xref="A1.E3.m2.4.4.3.3.3.3.cmml">,</mo><msub id="A1.E3.m2.4.4.3.3.3.2.2" xref="A1.E3.m2.4.4.3.3.3.2.2.cmml"><mi id="A1.E3.m2.4.4.3.3.3.2.2.2" xref="A1.E3.m2.4.4.3.3.3.2.2.2.cmml">y</mi><mi id="A1.E3.m2.4.4.3.3.3.2.2.3" xref="A1.E3.m2.4.4.3.3.3.2.2.3.cmml">n</mi></msub><mo stretchy="false" id="A1.E3.m2.4.4.3.3.3.2.5" xref="A1.E3.m2.4.4.3.3.3.3.cmml">)</mo></mrow><mo stretchy="false" id="A1.E3.m2.4.4.3.3.8" xref="A1.E3.m2.4.4.3.4.cmml">}</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.E3.m2.4b"><apply id="A1.E3.m2.4.4.cmml" xref="A1.E3.m2.4.4"><eq id="A1.E3.m2.4.4.4.cmml" xref="A1.E3.m2.4.4.4"></eq><csymbol cd="latexml" id="A1.E3.m2.4.4.5.cmml" xref="A1.E3.m2.4.4.5">absent</csymbol><set id="A1.E3.m2.4.4.3.4.cmml" xref="A1.E3.m2.4.4.3.3"><interval closure="open" id="A1.E3.m2.2.2.1.1.1.3.cmml" xref="A1.E3.m2.2.2.1.1.1.2"><apply id="A1.E3.m2.2.2.1.1.1.1.1.cmml" xref="A1.E3.m2.2.2.1.1.1.1.1"><csymbol cd="ambiguous" id="A1.E3.m2.2.2.1.1.1.1.1.1.cmml" xref="A1.E3.m2.2.2.1.1.1.1.1">subscript</csymbol><ci id="A1.E3.m2.2.2.1.1.1.1.1.2.cmml" xref="A1.E3.m2.2.2.1.1.1.1.1.2">𝑥</ci><cn type="integer" id="A1.E3.m2.2.2.1.1.1.1.1.3.cmml" xref="A1.E3.m2.2.2.1.1.1.1.1.3">1</cn></apply><apply id="A1.E3.m2.2.2.1.1.1.2.2.cmml" xref="A1.E3.m2.2.2.1.1.1.2.2"><csymbol cd="ambiguous" id="A1.E3.m2.2.2.1.1.1.2.2.1.cmml" xref="A1.E3.m2.2.2.1.1.1.2.2">subscript</csymbol><ci id="A1.E3.m2.2.2.1.1.1.2.2.2.cmml" xref="A1.E3.m2.2.2.1.1.1.2.2.2">𝑦</ci><cn type="integer" id="A1.E3.m2.2.2.1.1.1.2.2.3.cmml" xref="A1.E3.m2.2.2.1.1.1.2.2.3">1</cn></apply></interval><interval closure="open" id="A1.E3.m2.3.3.2.2.2.3.cmml" xref="A1.E3.m2.3.3.2.2.2.2"><apply id="A1.E3.m2.3.3.2.2.2.1.1.cmml" xref="A1.E3.m2.3.3.2.2.2.1.1"><csymbol cd="ambiguous" id="A1.E3.m2.3.3.2.2.2.1.1.1.cmml" xref="A1.E3.m2.3.3.2.2.2.1.1">subscript</csymbol><ci id="A1.E3.m2.3.3.2.2.2.1.1.2.cmml" xref="A1.E3.m2.3.3.2.2.2.1.1.2">𝑥</ci><cn type="integer" id="A1.E3.m2.3.3.2.2.2.1.1.3.cmml" xref="A1.E3.m2.3.3.2.2.2.1.1.3">2</cn></apply><apply id="A1.E3.m2.3.3.2.2.2.2.2.cmml" xref="A1.E3.m2.3.3.2.2.2.2.2"><csymbol cd="ambiguous" id="A1.E3.m2.3.3.2.2.2.2.2.1.cmml" xref="A1.E3.m2.3.3.2.2.2.2.2">subscript</csymbol><ci id="A1.E3.m2.3.3.2.2.2.2.2.2.cmml" xref="A1.E3.m2.3.3.2.2.2.2.2.2">𝑦</ci><cn type="integer" id="A1.E3.m2.3.3.2.2.2.2.2.3.cmml" xref="A1.E3.m2.3.3.2.2.2.2.2.3">2</cn></apply></interval><ci id="A1.E3.m2.1.1.cmml" xref="A1.E3.m2.1.1">…</ci><interval closure="open" id="A1.E3.m2.4.4.3.3.3.3.cmml" xref="A1.E3.m2.4.4.3.3.3.2"><apply id="A1.E3.m2.4.4.3.3.3.1.1.cmml" xref="A1.E3.m2.4.4.3.3.3.1.1"><csymbol cd="ambiguous" id="A1.E3.m2.4.4.3.3.3.1.1.1.cmml" xref="A1.E3.m2.4.4.3.3.3.1.1">subscript</csymbol><ci id="A1.E3.m2.4.4.3.3.3.1.1.2.cmml" xref="A1.E3.m2.4.4.3.3.3.1.1.2">𝑥</ci><ci id="A1.E3.m2.4.4.3.3.3.1.1.3.cmml" xref="A1.E3.m2.4.4.3.3.3.1.1.3">𝑛</ci></apply><apply id="A1.E3.m2.4.4.3.3.3.2.2.cmml" xref="A1.E3.m2.4.4.3.3.3.2.2"><csymbol cd="ambiguous" id="A1.E3.m2.4.4.3.3.3.2.2.1.cmml" xref="A1.E3.m2.4.4.3.3.3.2.2">subscript</csymbol><ci id="A1.E3.m2.4.4.3.3.3.2.2.2.cmml" xref="A1.E3.m2.4.4.3.3.3.2.2.2">𝑦</ci><ci id="A1.E3.m2.4.4.3.3.3.2.2.3.cmml" xref="A1.E3.m2.4.4.3.3.3.2.2.3">𝑛</ci></apply></interval></set></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E3.m2.4c">\displaystyle=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{n},y_{n})\}</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.3)</span></td>
</tr></tbody>
<tbody id="A1.E4"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E4.m1.1" class="ltx_Math" alttext="\displaystyle\mathcal{X}" display="inline"><semantics id="A1.E4.m1.1a"><mi class="ltx_font_mathcaligraphic" id="A1.E4.m1.1.1" xref="A1.E4.m1.1.1.cmml">𝒳</mi><annotation-xml encoding="MathML-Content" id="A1.E4.m1.1b"><ci id="A1.E4.m1.1.1.cmml" xref="A1.E4.m1.1.1">𝒳</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.E4.m1.1c">\displaystyle\mathcal{X}</annotation></semantics></math></td>
<td class="ltx_td ltx_align_left ltx_eqn_cell"><math id="A1.E4.m2.4" class="ltx_Math" alttext="\displaystyle=\{x^{*}_{1},x^{*}_{2},...,x^{*}_{m}\}" display="inline"><semantics id="A1.E4.m2.4a"><mrow id="A1.E4.m2.4.4" xref="A1.E4.m2.4.4.cmml"><mi id="A1.E4.m2.4.4.5" xref="A1.E4.m2.4.4.5.cmml"></mi><mo id="A1.E4.m2.4.4.4" xref="A1.E4.m2.4.4.4.cmml">=</mo><mrow id="A1.E4.m2.4.4.3.3" xref="A1.E4.m2.4.4.3.4.cmml"><mo stretchy="false" id="A1.E4.m2.4.4.3.3.4" xref="A1.E4.m2.4.4.3.4.cmml">{</mo><msubsup id="A1.E4.m2.2.2.1.1.1" xref="A1.E4.m2.2.2.1.1.1.cmml"><mi id="A1.E4.m2.2.2.1.1.1.2.2" xref="A1.E4.m2.2.2.1.1.1.2.2.cmml">x</mi><mn id="A1.E4.m2.2.2.1.1.1.3" xref="A1.E4.m2.2.2.1.1.1.3.cmml">1</mn><mo id="A1.E4.m2.2.2.1.1.1.2.3" xref="A1.E4.m2.2.2.1.1.1.2.3.cmml">∗</mo></msubsup><mo id="A1.E4.m2.4.4.3.3.5" xref="A1.E4.m2.4.4.3.4.cmml">,</mo><msubsup id="A1.E4.m2.3.3.2.2.2" xref="A1.E4.m2.3.3.2.2.2.cmml"><mi id="A1.E4.m2.3.3.2.2.2.2.2" xref="A1.E4.m2.3.3.2.2.2.2.2.cmml">x</mi><mn id="A1.E4.m2.3.3.2.2.2.3" xref="A1.E4.m2.3.3.2.2.2.3.cmml">2</mn><mo id="A1.E4.m2.3.3.2.2.2.2.3" xref="A1.E4.m2.3.3.2.2.2.2.3.cmml">∗</mo></msubsup><mo id="A1.E4.m2.4.4.3.3.6" xref="A1.E4.m2.4.4.3.4.cmml">,</mo><mi mathvariant="normal" id="A1.E4.m2.1.1" xref="A1.E4.m2.1.1.cmml">…</mi><mo id="A1.E4.m2.4.4.3.3.7" xref="A1.E4.m2.4.4.3.4.cmml">,</mo><msubsup id="A1.E4.m2.4.4.3.3.3" xref="A1.E4.m2.4.4.3.3.3.cmml"><mi id="A1.E4.m2.4.4.3.3.3.2.2" xref="A1.E4.m2.4.4.3.3.3.2.2.cmml">x</mi><mi id="A1.E4.m2.4.4.3.3.3.3" xref="A1.E4.m2.4.4.3.3.3.3.cmml">m</mi><mo id="A1.E4.m2.4.4.3.3.3.2.3" xref="A1.E4.m2.4.4.3.3.3.2.3.cmml">∗</mo></msubsup><mo stretchy="false" id="A1.E4.m2.4.4.3.3.8" xref="A1.E4.m2.4.4.3.4.cmml">}</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.E4.m2.4b"><apply id="A1.E4.m2.4.4.cmml" xref="A1.E4.m2.4.4"><eq id="A1.E4.m2.4.4.4.cmml" xref="A1.E4.m2.4.4.4"></eq><csymbol cd="latexml" id="A1.E4.m2.4.4.5.cmml" xref="A1.E4.m2.4.4.5">absent</csymbol><set id="A1.E4.m2.4.4.3.4.cmml" xref="A1.E4.m2.4.4.3.3"><apply id="A1.E4.m2.2.2.1.1.1.cmml" xref="A1.E4.m2.2.2.1.1.1"><csymbol cd="ambiguous" id="A1.E4.m2.2.2.1.1.1.1.cmml" xref="A1.E4.m2.2.2.1.1.1">subscript</csymbol><apply id="A1.E4.m2.2.2.1.1.1.2.cmml" xref="A1.E4.m2.2.2.1.1.1"><csymbol cd="ambiguous" id="A1.E4.m2.2.2.1.1.1.2.1.cmml" xref="A1.E4.m2.2.2.1.1.1">superscript</csymbol><ci id="A1.E4.m2.2.2.1.1.1.2.2.cmml" xref="A1.E4.m2.2.2.1.1.1.2.2">𝑥</ci><times id="A1.E4.m2.2.2.1.1.1.2.3.cmml" xref="A1.E4.m2.2.2.1.1.1.2.3"></times></apply><cn type="integer" id="A1.E4.m2.2.2.1.1.1.3.cmml" xref="A1.E4.m2.2.2.1.1.1.3">1</cn></apply><apply id="A1.E4.m2.3.3.2.2.2.cmml" xref="A1.E4.m2.3.3.2.2.2"><csymbol cd="ambiguous" id="A1.E4.m2.3.3.2.2.2.1.cmml" xref="A1.E4.m2.3.3.2.2.2">subscript</csymbol><apply id="A1.E4.m2.3.3.2.2.2.2.cmml" xref="A1.E4.m2.3.3.2.2.2"><csymbol cd="ambiguous" id="A1.E4.m2.3.3.2.2.2.2.1.cmml" xref="A1.E4.m2.3.3.2.2.2">superscript</csymbol><ci id="A1.E4.m2.3.3.2.2.2.2.2.cmml" xref="A1.E4.m2.3.3.2.2.2.2.2">𝑥</ci><times id="A1.E4.m2.3.3.2.2.2.2.3.cmml" xref="A1.E4.m2.3.3.2.2.2.2.3"></times></apply><cn type="integer" id="A1.E4.m2.3.3.2.2.2.3.cmml" xref="A1.E4.m2.3.3.2.2.2.3">2</cn></apply><ci id="A1.E4.m2.1.1.cmml" xref="A1.E4.m2.1.1">…</ci><apply id="A1.E4.m2.4.4.3.3.3.cmml" xref="A1.E4.m2.4.4.3.3.3"><csymbol cd="ambiguous" id="A1.E4.m2.4.4.3.3.3.1.cmml" xref="A1.E4.m2.4.4.3.3.3">subscript</csymbol><apply id="A1.E4.m2.4.4.3.3.3.2.cmml" xref="A1.E4.m2.4.4.3.3.3"><csymbol cd="ambiguous" id="A1.E4.m2.4.4.3.3.3.2.1.cmml" xref="A1.E4.m2.4.4.3.3.3">superscript</csymbol><ci id="A1.E4.m2.4.4.3.3.3.2.2.cmml" xref="A1.E4.m2.4.4.3.3.3.2.2">𝑥</ci><times id="A1.E4.m2.4.4.3.3.3.2.3.cmml" xref="A1.E4.m2.4.4.3.3.3.2.3"></times></apply><ci id="A1.E4.m2.4.4.3.3.3.3.cmml" xref="A1.E4.m2.4.4.3.3.3.3">𝑚</ci></apply></set></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E4.m2.4c">\displaystyle=\{x^{*}_{1},x^{*}_{2},...,x^{*}_{m}\}</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.4)</span></td>
</tr></tbody>
</table>
</div>
<div id="A1.S7.SS0.SSS0.Px3.p3" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px3.p3.2" class="ltx_p">In the few-shot setting, the prompt template function <math id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1" class="ltx_Math" alttext="\mathcal{T}(\cdot)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1a"><mrow id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.2" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.2.cmml">𝒯</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.1" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.3.2" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.cmml">(</mo><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.1.cmml">⋅</mo><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1b"><apply id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2"><times id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.2.2">𝒯</ci><ci id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.1.m1.1.1">⋅</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px3.p3.1.m1.1c">\mathcal{T}(\cdot)</annotation></semantics></math> also takes as input one or more training samples <math id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1" class="ltx_Math" alttext="\mathcal{X}=\{(x_{i},y_{i})\}_{1}^{n}\subset\mathcal{D}_{\text{train}}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1a"><mrow id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.3.cmml">𝒳</mi><mo id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.4" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.4.cmml">=</mo><msubsup id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.cmml"><mrow id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.2.cmml">{</mo><mrow id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.3.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.3.cmml">(</mo><msub id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.cmml"><mi id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.2.cmml">x</mi><mi id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.3.cmml">i</mi></msub><mo id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.4" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.3.cmml">,</mo><msub id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.cmml"><mi id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.2.cmml">y</mi><mi id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.3.cmml">i</mi></msub><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.5" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.3.cmml">)</mo></mrow><mo stretchy="false" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.2.cmml">}</mo></mrow><mn id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.3.cmml">1</mn><mi id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.3.cmml">n</mi></msubsup><mo id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.5" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.5.cmml">⊂</mo><msub id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.2" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.2.cmml">𝒟</mi><mtext id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3a.cmml">train</mtext></msub></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1b"><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1"><and id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1a.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1"></and><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1b.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1"><eq id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.4.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.4"></eq><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.3">𝒳</ci><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1">superscript</csymbol><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1">subscript</csymbol><set id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1"><interval closure="open" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2"><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.2">𝑥</ci><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.1.1.3">𝑖</ci></apply><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.2">𝑦</ci><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.1.1.1.2.2.3">𝑖</ci></apply></interval></set><cn type="integer" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.1.3">1</cn></apply><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.3">𝑛</ci></apply></apply><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1c.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1"><subset id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.5.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.5"></subset><share href="#A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.1.cmml" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1d.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1"></share><apply id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.1.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.2.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.2">𝒟</ci><ci id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3a.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3"><mtext mathsize="70%" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3.cmml" xref="A1.S7.SS0.SSS0.Px3.p3.2.m2.1.1.6.3">train</mtext></ci></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px3.p3.2.m2.1c">\mathcal{X}=\{(x_{i},y_{i})\}_{1}^{n}\subset\mathcal{D}_{\text{train}}</annotation></semantics></math></p>
</div>
<div id="A1.S7.SS0.SSS0.Px3.p4" class="ltx_para">
<table id="A1.S9.EGx4" class="ltx_equationgroup ltx_eqn_align ltx_eqn_table">

<tbody id="A1.E5"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E5.m1.5" class="ltx_Math" alttext="\displaystyle p\bigl{(}\mathcal{A}\mid\mathcal{T}\left(\mathcal{X},\ x^{*}\right)\bigr{)}=\prod_{i=1}^{|\mathcal{A}|}p_{\rm LM}\left(a_{i}\mid\mathcal{T}\left(\mathcal{X},\ x^{*}\right),a_{1:i-1}\right)" display="inline"><semantics id="A1.E5.m1.5a"><mrow id="A1.E5.m1.5.5" xref="A1.E5.m1.5.5.cmml"><mrow id="A1.E5.m1.4.4.1" xref="A1.E5.m1.4.4.1.cmml"><mi id="A1.E5.m1.4.4.1.3" xref="A1.E5.m1.4.4.1.3.cmml">p</mi><mo lspace="0em" rspace="0em" id="A1.E5.m1.4.4.1.2" xref="A1.E5.m1.4.4.1.2.cmml">​</mo><mrow id="A1.E5.m1.4.4.1.1.1" xref="A1.E5.m1.4.4.1.1.1.1.cmml"><mo maxsize="120%" minsize="120%" id="A1.E5.m1.4.4.1.1.1.2" xref="A1.E5.m1.4.4.1.1.1.1.cmml">(</mo><mrow id="A1.E5.m1.4.4.1.1.1.1" xref="A1.E5.m1.4.4.1.1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E5.m1.4.4.1.1.1.1.3" xref="A1.E5.m1.4.4.1.1.1.1.3.cmml">𝒜</mi><mo id="A1.E5.m1.4.4.1.1.1.1.2" 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xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2">subscript</csymbol><ci id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.2.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.2">𝑎</ci><apply id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3"><ci id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.1.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.1">:</ci><cn type="integer" id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.2.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.2">1</cn><apply id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3"><minus id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.1.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.1"></minus><ci id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.2.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.2">𝑖</ci><cn type="integer" id="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.3.cmml" xref="A1.E5.m1.5.5.2.1.1.1.1.2.2.2.3.3.3">1</cn></apply></apply></apply></list></apply></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E5.m1.5c">\displaystyle p\bigl{(}\mathcal{A}\mid\mathcal{T}\left(\mathcal{X},\ x^{*}\right)\bigr{)}=\prod_{i=1}^{|\mathcal{A}|}p_{\rm LM}\left(a_{i}\mid\mathcal{T}\left(\mathcal{X},\ x^{*}\right),a_{1:i-1}\right)</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.5)</span></td>
</tr></tbody>
</table>
</div>
</section>
<section id="A1.S7.SS0.SSS0.Px4" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Optimization.</h6>

<div id="A1.S7.SS0.SSS0.Px4.p1" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px4.p1.1" class="ltx_p">As mentioned, it is often desirable to speak about improving prompts (prompt templates, that is) with respect to a scoring function, usually defined with respect to a dataset.</p>
</div>
<div id="A1.S7.SS0.SSS0.Px4.p2" class="ltx_para">
<table id="A1.E6" class="ltx_equation ltx_eqn_table">

<tbody><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
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xref="A1.E6.m1.3.3.1.1.1.1.2.2.2.3">𝑖</ci></apply></interval></apply></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E6.m1.3c">\mathcal{T}^{*}=\underset{\mathcal{T}}{\mathrm{argmax}}\;\mathbb{E}_{x_{i},y_{i}\sim\mathcal{D}}\left[S\left(p_{\text{LM}}(\mathcal{A}|\mathcal{T}(x_{i})),y_{i}\right)\right]</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.6)</span></td>
</tr></tbody>
</table>
</div>
<div id="A1.S7.SS0.SSS0.Px4.p3" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px4.p3.7" class="ltx_p">In this definition, we are evaluating over a dataset <math id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1" class="ltx_Math" alttext="\mathcal{D}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.1.m1.1.1.cmml">𝒟</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1b"><ci id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.1.m1.1.1">𝒟</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.1.m1.1c">\mathcal{D}</annotation></semantics></math> with respect to the scoring function <math id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1" class="ltx_Math" alttext="S(\cdot)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1a"><mrow id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.cmml"><mi id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.2" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.2.cmml">S</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.1" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.3.2" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.cmml">(</mo><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.1.cmml">⋅</mo><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1b"><apply id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2"><times id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.2.2">𝑆</ci><ci id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.2.m2.1.1">⋅</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.2.m2.1c">S(\cdot)</annotation></semantics></math>. <math id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1" class="ltx_Math" alttext="S(\cdot)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1a"><mrow id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.cmml"><mi id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.2" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.2.cmml">S</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.1" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.3.2" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.cmml">(</mo><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.1.cmml">⋅</mo><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1b"><apply id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2"><times id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.2.2">𝑆</ci><ci id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.3.m3.1.1">⋅</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.3.m3.1c">S(\cdot)</annotation></semantics></math> evaluates the output <math id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1" class="ltx_Math" alttext="\mathcal{A}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1a"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.4.m4.1.1.cmml">𝒜</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1b"><ci id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.4.m4.1.1">𝒜</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.4.m4.1c">\mathcal{A}</annotation></semantics></math>, generated by the LLM conditioned on the prompt <math id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1" class="ltx_Math" alttext="\mathcal{T(x_{i})}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1a"><mrow id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.3" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.3.cmml">𝒯</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.2.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.cmml">(</mo><msub id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.2.cmml">𝓍</mi><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.3" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.3.cmml">𝒾</mi></msub><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.3" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1b"><apply id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1"><times id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.2"></times><ci id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.3">𝒯</ci><apply id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.2">𝓍</ci><ci id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.5.m5.1.1.1.1.1.3">𝒾</ci></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.5.m5.1c">\mathcal{T(x_{i})}</annotation></semantics></math>. <math id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1" class="ltx_Math" alttext="y_{i}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1a"><msub id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.cmml"><mi id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.2" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.2.cmml">y</mi><mi id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.3" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1b"><apply id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.2">𝑦</ci><ci id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.6.m6.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.6.m6.1c">y_{i}</annotation></semantics></math> are labeled outputs that can be used by <math id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1" class="ltx_Math" alttext="S" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1a"><mi id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1.1" xref="A1.S7.SS0.SSS0.Px4.p3.7.m7.1.1.cmml">S</mi><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1b"><ci id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p3.7.m7.1.1">𝑆</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p3.7.m7.1c">S</annotation></semantics></math>.</p>
</div>
<div id="A1.S7.SS0.SSS0.Px4.p4" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px4.p4.2" class="ltx_p">In some cases, there may not be any labeled data <math id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1" class="ltx_Math" alttext="y_{i}" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1a"><msub id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.cmml"><mi id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.2" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.2.cmml">y</mi><mi id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.3" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.3.cmml">i</mi></msub><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1b"><apply id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1"><csymbol cd="ambiguous" id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1">subscript</csymbol><ci id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.2">𝑦</ci><ci id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.3.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.1.m1.1.1.3">𝑖</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p4.1.m1.1c">y_{i}</annotation></semantics></math>, and <math id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1" class="ltx_Math" alttext="S(\cdot)" display="inline"><semantics id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1a"><mrow id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.cmml"><mi id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.2" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.2.cmml">S</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.1" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.3.2" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.cmml">(</mo><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.1" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.1.cmml">⋅</mo><mo stretchy="false" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1b"><apply id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2"><times id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.2.2">𝑆</ci><ci id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.1.cmml" xref="A1.S7.SS0.SSS0.Px4.p4.2.m2.1.1">⋅</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px4.p4.2.m2.1c">S(\cdot)</annotation></semantics></math> may be reference-free.</p>
</div>
</section>
<section id="A1.S7.SS0.SSS0.Px5" class="ltx_paragraph">
<h6 class="ltx_title ltx_title_paragraph">Other considerations.</h6>

<div id="A1.S7.SS0.SSS0.Px5.p1" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px5.p1.1" class="ltx_p">These formalisms could be adapted to cater to CoT, retrieval systems, and more. Here we describe a simple setup which is most descriptive of the prompting process without adding too much complexity.</p>
</div>
<div id="A1.S7.SS0.SSS0.Px5.p2" class="ltx_para">
<p id="A1.S7.SS0.SSS0.Px5.p2.1" class="ltx_p">We also draw attention to the lesser known concept of answer engineering. <math id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1" class="ltx_Math" alttext="E(\mathcal{A})" display="inline"><semantics id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1a"><mrow id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.cmml"><mi id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.2" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.2.cmml">E</mi><mo lspace="0em" rspace="0em" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.1" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.1.cmml">​</mo><mrow id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.3.2" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.cmml"><mo stretchy="false" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.3.2.1" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.cmml">(</mo><mi class="ltx_font_mathcaligraphic" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.1" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.1.cmml">𝒜</mi><mo stretchy="false" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.3.2.2" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.cmml">)</mo></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1b"><apply id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.cmml" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2"><times id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.1.cmml" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.1"></times><ci id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.2.cmml" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.2.2">𝐸</ci><ci id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.1.cmml" xref="A1.S7.SS0.SSS0.Px5.p2.1.m1.1.1">𝒜</ci></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.S7.SS0.SSS0.Px5.p2.1.m1.1c">E(\mathcal{A})</annotation></semantics></math> is a transformation function over the raw LLM output that allows it to be compared to the ground truth.</p>
</div>
<div id="A1.S7.SS0.SSS0.Px5.p3" class="ltx_para">
<table id="A1.S9.EGx5" class="ltx_equationgroup ltx_eqn_align ltx_eqn_table">

<tbody id="A1.E7"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E7.m1.1" class="ltx_Math" alttext="\displaystyle\mathcal{A}" display="inline"><semantics id="A1.E7.m1.1a"><mi class="ltx_font_mathcaligraphic" id="A1.E7.m1.1.1" xref="A1.E7.m1.1.1.cmml">𝒜</mi><annotation-xml encoding="MathML-Content" id="A1.E7.m1.1b"><ci id="A1.E7.m1.1.1.cmml" xref="A1.E7.m1.1.1">𝒜</ci></annotation-xml><annotation encoding="application/x-tex" id="A1.E7.m1.1c">\displaystyle\mathcal{A}</annotation></semantics></math></td>
<td class="ltx_td ltx_align_left ltx_eqn_cell"><math id="A1.E7.m2.1" class="ltx_Math" alttext="\displaystyle\sim p_{\text{LM}}(\mathcal{A}\mid\mathcal{T}(x_{i}),y_{i})" display="inline"><semantics id="A1.E7.m2.1a"><mrow id="A1.E7.m2.1.1" xref="A1.E7.m2.1.1.cmml"><mi id="A1.E7.m2.1.1.3" xref="A1.E7.m2.1.1.3.cmml"></mi><mo id="A1.E7.m2.1.1.2" xref="A1.E7.m2.1.1.2.cmml">∼</mo><mrow id="A1.E7.m2.1.1.1" xref="A1.E7.m2.1.1.1.cmml"><msub id="A1.E7.m2.1.1.1.3" xref="A1.E7.m2.1.1.1.3.cmml"><mi id="A1.E7.m2.1.1.1.3.2" xref="A1.E7.m2.1.1.1.3.2.cmml">p</mi><mtext id="A1.E7.m2.1.1.1.3.3" xref="A1.E7.m2.1.1.1.3.3a.cmml">LM</mtext></msub><mo lspace="0em" rspace="0em" id="A1.E7.m2.1.1.1.2" xref="A1.E7.m2.1.1.1.2.cmml">​</mo><mrow id="A1.E7.m2.1.1.1.1.1" xref="A1.E7.m2.1.1.1.1.1.1.cmml"><mo stretchy="false" id="A1.E7.m2.1.1.1.1.1.2" xref="A1.E7.m2.1.1.1.1.1.1.cmml">(</mo><mrow id="A1.E7.m2.1.1.1.1.1.1" xref="A1.E7.m2.1.1.1.1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E7.m2.1.1.1.1.1.1.4" xref="A1.E7.m2.1.1.1.1.1.1.4.cmml">𝒜</mi><mo id="A1.E7.m2.1.1.1.1.1.1.3" xref="A1.E7.m2.1.1.1.1.1.1.3.cmml">∣</mo><mrow id="A1.E7.m2.1.1.1.1.1.1.2.2" xref="A1.E7.m2.1.1.1.1.1.1.2.3.cmml"><mrow id="A1.E7.m2.1.1.1.1.1.1.1.1.1" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E7.m2.1.1.1.1.1.1.1.1.1.3" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.3.cmml">𝒯</mi><mo lspace="0em" rspace="0em" id="A1.E7.m2.1.1.1.1.1.1.1.1.1.2" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.2.cmml">​</mo><mrow id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.cmml"><mo stretchy="false" id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.2" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.cmml">(</mo><msub id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.cmml"><mi id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.2" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.2.cmml">x</mi><mi id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.3" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.3.cmml">i</mi></msub><mo stretchy="false" id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.3" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.cmml">)</mo></mrow></mrow><mo id="A1.E7.m2.1.1.1.1.1.1.2.2.3" xref="A1.E7.m2.1.1.1.1.1.1.2.3.cmml">,</mo><msub id="A1.E7.m2.1.1.1.1.1.1.2.2.2" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2.cmml"><mi id="A1.E7.m2.1.1.1.1.1.1.2.2.2.2" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2.2.cmml">y</mi><mi id="A1.E7.m2.1.1.1.1.1.1.2.2.2.3" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2.3.cmml">i</mi></msub></mrow></mrow><mo stretchy="false" id="A1.E7.m2.1.1.1.1.1.3" xref="A1.E7.m2.1.1.1.1.1.1.cmml">)</mo></mrow></mrow></mrow><annotation-xml encoding="MathML-Content" id="A1.E7.m2.1b"><apply id="A1.E7.m2.1.1.cmml" xref="A1.E7.m2.1.1"><csymbol cd="latexml" id="A1.E7.m2.1.1.2.cmml" xref="A1.E7.m2.1.1.2">similar-to</csymbol><csymbol cd="latexml" id="A1.E7.m2.1.1.3.cmml" xref="A1.E7.m2.1.1.3">absent</csymbol><apply id="A1.E7.m2.1.1.1.cmml" xref="A1.E7.m2.1.1.1"><times id="A1.E7.m2.1.1.1.2.cmml" xref="A1.E7.m2.1.1.1.2"></times><apply id="A1.E7.m2.1.1.1.3.cmml" xref="A1.E7.m2.1.1.1.3"><csymbol cd="ambiguous" id="A1.E7.m2.1.1.1.3.1.cmml" xref="A1.E7.m2.1.1.1.3">subscript</csymbol><ci id="A1.E7.m2.1.1.1.3.2.cmml" xref="A1.E7.m2.1.1.1.3.2">𝑝</ci><ci id="A1.E7.m2.1.1.1.3.3a.cmml" xref="A1.E7.m2.1.1.1.3.3"><mtext mathsize="70%" id="A1.E7.m2.1.1.1.3.3.cmml" xref="A1.E7.m2.1.1.1.3.3">LM</mtext></ci></apply><apply id="A1.E7.m2.1.1.1.1.1.1.cmml" xref="A1.E7.m2.1.1.1.1.1"><csymbol cd="latexml" id="A1.E7.m2.1.1.1.1.1.1.3.cmml" xref="A1.E7.m2.1.1.1.1.1.1.3">conditional</csymbol><ci id="A1.E7.m2.1.1.1.1.1.1.4.cmml" xref="A1.E7.m2.1.1.1.1.1.1.4">𝒜</ci><list id="A1.E7.m2.1.1.1.1.1.1.2.3.cmml" xref="A1.E7.m2.1.1.1.1.1.1.2.2"><apply id="A1.E7.m2.1.1.1.1.1.1.1.1.1.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1"><times id="A1.E7.m2.1.1.1.1.1.1.1.1.1.2.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.2"></times><ci id="A1.E7.m2.1.1.1.1.1.1.1.1.1.3.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.3">𝒯</ci><apply id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1"><csymbol cd="ambiguous" id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.1.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1">subscript</csymbol><ci id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.2.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.2">𝑥</ci><ci id="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.3.cmml" xref="A1.E7.m2.1.1.1.1.1.1.1.1.1.1.1.1.3">𝑖</ci></apply></apply><apply id="A1.E7.m2.1.1.1.1.1.1.2.2.2.cmml" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2"><csymbol cd="ambiguous" id="A1.E7.m2.1.1.1.1.1.1.2.2.2.1.cmml" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2">subscript</csymbol><ci id="A1.E7.m2.1.1.1.1.1.1.2.2.2.2.cmml" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2.2">𝑦</ci><ci id="A1.E7.m2.1.1.1.1.1.1.2.2.2.3.cmml" xref="A1.E7.m2.1.1.1.1.1.1.2.2.2.3">𝑖</ci></apply></list></apply></apply></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E7.m2.1c">\displaystyle\sim p_{\text{LM}}(\mathcal{A}\mid\mathcal{T}(x_{i}),y_{i})</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.7)</span></td>
</tr></tbody>
<tbody id="A1.E8"><tr class="ltx_equation ltx_eqn_row ltx_align_baseline">
<td class="ltx_eqn_cell ltx_eqn_center_padleft"></td>
<td class="ltx_td ltx_align_right ltx_eqn_cell"><math id="A1.E8.m1.1" class="ltx_Math" alttext="\displaystyle\mathcal{T}^{*}" display="inline"><semantics id="A1.E8.m1.1a"><msup id="A1.E8.m1.1.1" xref="A1.E8.m1.1.1.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E8.m1.1.1.2" xref="A1.E8.m1.1.1.2.cmml">𝒯</mi><mo id="A1.E8.m1.1.1.3" xref="A1.E8.m1.1.1.3.cmml">∗</mo></msup><annotation-xml encoding="MathML-Content" id="A1.E8.m1.1b"><apply id="A1.E8.m1.1.1.cmml" xref="A1.E8.m1.1.1"><csymbol cd="ambiguous" id="A1.E8.m1.1.1.1.cmml" xref="A1.E8.m1.1.1">superscript</csymbol><ci id="A1.E8.m1.1.1.2.cmml" xref="A1.E8.m1.1.1.2">𝒯</ci><times id="A1.E8.m1.1.1.3.cmml" xref="A1.E8.m1.1.1.3"></times></apply></annotation-xml><annotation encoding="application/x-tex" id="A1.E8.m1.1c">\displaystyle\mathcal{T}^{*}</annotation></semantics></math></td>
<td class="ltx_td ltx_align_left ltx_eqn_cell"><math id="A1.E8.m2.6" class="ltx_Math" alttext="\displaystyle=\underset{\mathcal{T},E}{\mathrm{argmax}}\;\mathbb{E}_{x_{i},y_{i}\sim\mathcal{D}}\left[S\left(E(\mathcal{A}),y_{i}\right)\right]" display="inline"><semantics id="A1.E8.m2.6a"><mrow id="A1.E8.m2.6.6" xref="A1.E8.m2.6.6.cmml"><mi id="A1.E8.m2.6.6.3" xref="A1.E8.m2.6.6.3.cmml"></mi><mo id="A1.E8.m2.6.6.2" xref="A1.E8.m2.6.6.2.cmml">=</mo><mrow id="A1.E8.m2.6.6.1" xref="A1.E8.m2.6.6.1.cmml"><munder accentunder="true" id="A1.E8.m2.2.2" xref="A1.E8.m2.2.2.cmml"><mi id="A1.E8.m2.2.2.3" xref="A1.E8.m2.2.2.3.cmml">argmax</mi><mrow id="A1.E8.m2.2.2.2.4" xref="A1.E8.m2.2.2.2.3.cmml"><mi class="ltx_font_mathcaligraphic" id="A1.E8.m2.1.1.1.1" xref="A1.E8.m2.1.1.1.1.cmml">𝒯</mi><mo id="A1.E8.m2.2.2.2.4.1" xref="A1.E8.m2.2.2.2.3.cmml">,</mo><mi id="A1.E8.m2.2.2.2.2" xref="A1.E8.m2.2.2.2.2.cmml">E</mi></mrow></munder><mo lspace="0em" rspace="0em" id="A1.E8.m2.6.6.1.2" 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id="A1.E8.m2.6c">\displaystyle=\underset{\mathcal{T},E}{\mathrm{argmax}}\;\mathbb{E}_{x_{i},y_{i}\sim\mathcal{D}}\left[S\left(E(\mathcal{A}),y_{i}\right)\right]</annotation></semantics></math></td>
<td class="ltx_eqn_cell ltx_eqn_center_padright"></td>
<td rowspan="1" class="ltx_eqn_cell ltx_eqn_eqno ltx_align_middle ltx_align_right"><span class="ltx_tag ltx_tag_equation ltx_align_right">(A.8)</span></td>
</tr></tbody>
</table>
</div>
</section>
</section>
<section id="A1.S8" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.8 </span>In-Context Learning Definitions Disambiguation</h3>

<div id="A1.S8.p1" class="ltx_para">
<p id="A1.S8.p1.1" class="ltx_p"><cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite> seemingly offer two different definitions for ICL. All bolding in this section is our own.</p>
</div>
<div id="A1.S8.p2" class="ltx_para">
<blockquote id="A1.S8.p2.1" class="ltx_quote">
<p id="A1.S8.p2.1.1" class="ltx_p">Recent work [RWC+19] attempts to do this via what we call “in-context learning”, using the text input of a pretrained language model as a form of task specification: the model is <span id="A1.S8.p2.1.1.1" class="ltx_text ltx_font_bold">conditioned on a natural language instruction and/or a few demonstrations of the task</span> and is then expected to complete further instances of the task simply by predicting what comes next.</p>
</blockquote>
</div>
<div id="A1.S8.p3" class="ltx_para">
<p id="A1.S8.p3.1" class="ltx_p">However, they later appear to define it as few-shot only:</p>
</div>
<div id="A1.S8.p4" class="ltx_para">
<blockquote id="A1.S8.p4.1" class="ltx_quote">
<p id="A1.S8.p4.1.1" class="ltx_p">For each task, we evaluate GPT-3 under 3 conditions: (a) <span id="A1.S8.p4.1.1.1" class="ltx_text ltx_font_bold">“few-shot learning”, or in-context learning where we allow as many demonstrations as will fit into the model’s context window</span> (typically 10 to 100), (b) “one-shot learning”, where we allow only one demonstration, and (c) “zero-shot” learning, where no demonstrations are allowed and only an instruction in natural language is given to the model.</p>
</blockquote>
</div>
<div id="A1.S8.p5" class="ltx_para">
<p id="A1.S8.p5.1" class="ltx_p">However, they include this image that clarifies the matter:</p>
</div>
<figure id="A1.F1" class="ltx_figure"><img src="/html/2406.06608/assets/assets/eval_strategies.png" id="A1.F1.g1" class="ltx_graphics ltx_centering ltx_img_square" width="598" height="556" alt="Refer to caption">
<figcaption class="ltx_caption ltx_centering"><span class="ltx_tag ltx_tag_figure">Figure A.1: </span>ICL from <cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>.</figcaption>
</figure>
<div id="A1.S8.p6" class="ltx_para">
<p id="A1.S8.p6.1" class="ltx_p">Additionally, they explicitly state that ICL does not necessarily involve learning new tasks.</p>
</div>
<div id="A1.S8.p7" class="ltx_para">
<blockquote id="A1.S8.p7.1" class="ltx_quote">
<p id="A1.S8.p7.1.1" class="ltx_p">To avoid this confusion, we use the term “meta-learning” to capture the inner-loop / outer-loop structure of the general method, and the term “in context-learning” to refer to the inner loop of meta-learning. We further specialize the description to “zero-shot”, “one-shot”, or “few-shot” depending on how many demonstrations are provided at inference time. <span id="A1.S8.p7.1.1.1" class="ltx_text ltx_font_bold">These terms are intended to remain agnostic on the question of whether the model learns new tasks from scratch at inference time or simply recognizes patterns seen during training</span> – this is an important issue which we discuss later in the paper, but “meta-learning” is intended to encompass both possibilities, and simply describes the inner-outer loop structure.</p>
</blockquote>
</div>
<div id="A1.S8.p8" class="ltx_para">
<p id="A1.S8.p8.1" class="ltx_p">We use <cite class="ltx_cite ltx_citemacro_citet">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>’s broad definition, though note that practitioners often use ICL to refer to situations in which the model appears to be learning new tasks from the prompt. Our definition differs from <cite class="ltx_cite ltx_citemacro_citet">Dong et al. (<a href="#bib.bib50" title="" class="ltx_ref">2023</a>)</cite>’s formal definition, even though it is also derived from <cite class="ltx_cite ltx_citemacro_cite">Brown et al. (<a href="#bib.bib22" title="" class="ltx_ref">2020</a>)</cite>.</p>
</div>
</section>
<section id="A1.S9" class="ltx_section">
<h3 class="ltx_title ltx_title_section">
<span class="ltx_tag ltx_tag_section">A.9 </span>Contributions</h3>

<div id="A1.S9.p1" class="ltx_para ltx_noindent">
<p id="A1.S9.p1.1" class="ltx_p">The following are the contributions made by the team members in various sections of this paper. Most authors conducted reviews of other sections as well.</p>
</div>
<section id="A1.S9.SSx1" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">Advisors</h4>

<div id="A1.S9.SSx1.p1" class="ltx_para">
<ul id="A1.S9.I1" class="ltx_itemize">
<li id="A1.S9.I1.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i1.p1" class="ltx_para">
<p id="A1.S9.I1.i1.p1.1" class="ltx_p"><span id="A1.S9.I1.i1.p1.1.1" class="ltx_text ltx_font_bold">Denis Peskoff:</span> Assisted with paper organization and final review.</p>
</div>
</li>
<li id="A1.S9.I1.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i2.p1" class="ltx_para">
<p id="A1.S9.I1.i2.p1.1" class="ltx_p"><span id="A1.S9.I1.i2.p1.1.1" class="ltx_text ltx_font_bold">Alexander Hoyle:</span> Provided guidance on writing, meta-analysis approach, and ran automated baselines for case study.</p>
</div>
</li>
<li id="A1.S9.I1.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i3.p1" class="ltx_para">
<p id="A1.S9.I1.i3.p1.1" class="ltx_p"><span id="A1.S9.I1.i3.p1.1.1" class="ltx_text ltx_font_bold">Shyamal Anadkat:</span> Assisted with the overall review of the paper and the etymology and definitions.</p>
</div>
</li>
<li id="A1.S9.I1.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i4.p1" class="ltx_para">
<p id="A1.S9.I1.i4.p1.1" class="ltx_p"><span id="A1.S9.I1.i4.p1.1.1" class="ltx_text ltx_font_bold">Jules White:</span> Built trees for technique taxonomies.</p>
</div>
</li>
<li id="A1.S9.I1.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i5.p1" class="ltx_para">
<p id="A1.S9.I1.i5.p1.1" class="ltx_p"><span id="A1.S9.I1.i5.p1.1.1" class="ltx_text ltx_font_bold">Marine Carpaut:</span> Framed, reviewed and suggested papers for the multilingual section.</p>
</div>
</li>
<li id="A1.S9.I1.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I1.i6.p1" class="ltx_para">
<p id="A1.S9.I1.i6.p1.1" class="ltx_p"><span id="A1.S9.I1.i6.p1.1.1" class="ltx_text ltx_font_bold">Phillip Resnik:</span> Principal Investigator</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S9.SSx2" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">SCS Labeling</h4>

<div id="A1.S9.SSx2.p1" class="ltx_para">
<ul id="A1.S9.I2" class="ltx_itemize">
<li id="A1.S9.I2.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I2.i1.p1" class="ltx_para">
<p id="A1.S9.I2.i1.p1.1" class="ltx_p"><span id="A1.S9.I2.i1.p1.1.1" class="ltx_text ltx_font_bold">Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker:</span> reviewed and gave advice for this section.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S9.SSx3" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">Benchmarking and Agents</h4>

<div id="A1.S9.SSx3.p1" class="ltx_para">
<ul id="A1.S9.I3" class="ltx_itemize">
<li id="A1.S9.I3.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I3.i1.p1" class="ltx_para">
<p id="A1.S9.I3.i1.p1.1" class="ltx_p"><span id="A1.S9.I3.i1.p1.1.1" class="ltx_text ltx_font_bold">Konstantine Kahadze:</span> Team leader for the Benchmarking section; managed MMLU benchmarking codebase, contributed to Security and Meta Analysis.</p>
</div>
</li>
<li id="A1.S9.I3.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I3.i2.p1" class="ltx_para">
<p id="A1.S9.I3.i2.p1.1" class="ltx_p"><span id="A1.S9.I3.i2.p1.1.1" class="ltx_text ltx_font_bold">Ashay Srivastava:</span> Team leader for the Agents section, reviewed papers for human review, worked on the tool use agents section. Worked on the compilation of contributions.</p>
</div>
</li>
<li id="A1.S9.I3.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I3.i3.p1" class="ltx_para">
<p id="A1.S9.I3.i3.p1.1" class="ltx_p"><span id="A1.S9.I3.i3.p1.1.1" class="ltx_text ltx_font_bold">Hevander Da Costa</span>: Contributed to the Benchmarking section and Meta Review datasets list, reviewed literature on LLM code generation and prompting techniques. Added literature review content to the Agents section.</p>
</div>
</li>
<li id="A1.S9.I3.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I3.i4.p1" class="ltx_para">
<p id="A1.S9.I3.i4.p1.1" class="ltx_p"><span id="A1.S9.I3.i4.p1.1.1" class="ltx_text ltx_font_bold">Feileen Li:</span> Worked on the tool use agents section, assisted with the human paper review.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S9.SSx4" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">Alignment and Security</h4>

<div id="A1.S9.SSx4.p1" class="ltx_para">
<ul id="A1.S9.I4" class="ltx_itemize">
<li id="A1.S9.I4.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I4.i1.p1" class="ltx_para">
<p id="A1.S9.I4.i1.p1.1" class="ltx_p"><span id="A1.S9.I4.i1.p1.1.1" class="ltx_text ltx_font_bold">Nishant Balepur:</span> Team leader for the alignment section, helped with high-level discussions in benchmarking, and reviewed drafts.</p>
</div>
</li>
<li id="A1.S9.I4.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I4.i2.p1" class="ltx_para">
<p id="A1.S9.I4.i2.p1.1" class="ltx_p"><span id="A1.S9.I4.i2.p1.1.1" class="ltx_text ltx_font_bold">Sevien Schulhoff:</span> Team leader for the security section and contributed to the benchmarking section.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S9.SSx5" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">Related Works and Section Contributions</h4>

<div id="A1.S9.SSx5.p1" class="ltx_para">
<ul id="A1.S9.I5" class="ltx_itemize">
<li id="A1.S9.I5.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i1.p1" class="ltx_para">
<p id="A1.S9.I5.i1.p1.1" class="ltx_p"><span id="A1.S9.I5.i1.p1.1.1" class="ltx_text ltx_font_bold">Chenglei Si:</span> Suggested related works and edited section 2.2 and section 7.</p>
</div>
</li>
<li id="A1.S9.I5.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i2.p1" class="ltx_para">
<p id="A1.S9.I5.i2.p1.1" class="ltx_p"><span id="A1.S9.I5.i2.p1.1.1" class="ltx_text ltx_font_bold">Pranav Sandeep Dulepet:</span> Contributed definitions for section 2 and worked on segmentation and object detection in the multimodal section.</p>
</div>
</li>
<li id="A1.S9.I5.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i3.p1" class="ltx_para">
<p id="A1.S9.I5.i3.p1.1" class="ltx_p"><span id="A1.S9.I5.i3.p1.1.1" class="ltx_text ltx_font_bold">HyoJung Han:</span> Contributed to the Multimodal section, especially the speech+text part, and wrote the audio prompting section.</p>
</div>
</li>
<li id="A1.S9.I5.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i4.p1" class="ltx_para">
<p id="A1.S9.I5.i4.p1.1" class="ltx_p"><span id="A1.S9.I5.i4.p1.1.1" class="ltx_text ltx_font_bold">Hudson Tao:</span> Authored sections on image, video, and 3D within multimodal, reviewed papers for human review; maintained GitHub codebase, and built the project website.</p>
</div>
</li>
<li id="A1.S9.I5.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i5.p1" class="ltx_para">
<p id="A1.S9.I5.i5.p1.1" class="ltx_p"><span id="A1.S9.I5.i5.p1.1.1" class="ltx_text ltx_font_bold">Amanda Liu:</span> Authored taxonomic ontology sections, conducted background research for introduction and related work, developed code pipelines for meta-analysis graphs</p>
</div>
</li>
<li id="A1.S9.I5.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i6.p1" class="ltx_para">
<p id="A1.S9.I5.i6.p1.1" class="ltx_p"><span id="A1.S9.I5.i6.p1.1.1" class="ltx_text ltx_font_bold">Sweta Agrawal:</span> Team lead for evaluation section.</p>
</div>
</li>
<li id="A1.S9.I5.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i7.p1" class="ltx_para">
<p id="A1.S9.I5.i7.p1.1" class="ltx_p"><span id="A1.S9.I5.i7.p1.1.1" class="ltx_text ltx_font_bold">Saurav Vidyadhara:</span> Assisted with general review and revising taxonomy trees.</p>
</div>
</li>
<li id="A1.S9.I5.i8" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I5.i8.p1" class="ltx_para">
<p id="A1.S9.I5.i8.p1.1" class="ltx_p"><span id="A1.S9.I5.i8.p1.1.1" class="ltx_text ltx_font_bold">Chau Pham:</span> Assisted with meta review, including automated analysis of topics.</p>
</div>
</li>
</ul>
</div>
</section>
<section id="A1.S9.SSx6" class="ltx_subsection">
<h4 class="ltx_title ltx_title_subsection">Multilingual Prompting and Meta Analysis</h4>

<div id="A1.S9.SSx6.p1" class="ltx_para">
<ul id="A1.S9.I6" class="ltx_itemize">
<li id="A1.S9.I6.i1" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i1.p1" class="ltx_para">
<p id="A1.S9.I6.i1.p1.1" class="ltx_p"><span id="A1.S9.I6.i1.p1.1.1" class="ltx_text ltx_font_bold">Zoey Ki:</span> Led the Multilingual prompting section, conducted review on related papers, and wrote Section 3.1.</p>
</div>
</li>
<li id="A1.S9.I6.i2" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i2.p1" class="ltx_para">
<p id="A1.S9.I6.i2.p1.1" class="ltx_p"><span id="A1.S9.I6.i2.p1.1.1" class="ltx_text ltx_font_bold">Yinheng Li:</span> Worked on section 2.2 text-based techniques, reviewed techniques, and contributed to drafting figure 2.2.</p>
</div>
</li>
<li id="A1.S9.I6.i3" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i3.p1" class="ltx_para">
<p id="A1.S9.I6.i3.p1.1" class="ltx_p"><span id="A1.S9.I6.i3.p1.1.1" class="ltx_text ltx_font_bold">Saloni Gupta:</span> Wrote tests for paper compilation, helped set up paper pipeline, and worked on the code diagram and grammar for the paper.</p>
</div>
</li>
<li id="A1.S9.I6.i4" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i4.p1" class="ltx_para">
<p id="A1.S9.I6.i4.p1.1" class="ltx_p"><span id="A1.S9.I6.i4.p1.1.1" class="ltx_text ltx_font_bold">Gerson Kroiz:</span> Involved with section 1.1 and defining a prompt.</p>
</div>
</li>
<li id="A1.S9.I6.i5" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i5.p1" class="ltx_para">
<p id="A1.S9.I6.i5.p1.1" class="ltx_p"><span id="A1.S9.I6.i5.p1.1.1" class="ltx_text ltx_font_bold">Aayush Gupta:</span> Contributed to the Meta Analysis, compiling papers, and generating visualization graphs.</p>
</div>
</li>
<li id="A1.S9.I6.i6" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i6.p1" class="ltx_para">
<p id="A1.S9.I6.i6.p1.1" class="ltx_p"><span id="A1.S9.I6.i6.p1.1.1" class="ltx_text ltx_font_bold">Michael Ilie:</span> Co-Lead Author, managed codebase, ran experiments, collected data, and helped with various sections including the PRISMA review figure and the SCS prompting case study.</p>
</div>
</li>
<li id="A1.S9.I6.i7" class="ltx_item" style="list-style-type:none;">
<span class="ltx_tag ltx_tag_item">•</span> 
<div id="A1.S9.I6.i7.p1" class="ltx_para">
<p id="A1.S9.I6.i7.p1.1" class="ltx_p"><span id="A1.S9.I6.i7.p1.1.1" class="ltx_text ltx_font_bold">Sander Schulhoff:</span> Lead Author</p>
</div>
</li>
</ul>
</div>
<div class="ltx_pagination ltx_role_newpage"></div>
</section>
</section>
</section>
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2406.06608
Generative Artificial Intelligence (GenAI)  systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area’s nascency. This paper establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. We present a comprehensive vocabulary of 33 vocabulary terms, a taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities. We further present a meta-analysis of the entire literature on natural language prefix-prompting.
abstract
The Prompt Report: A Systematic Survey of Prompting Techniques
Abstract
2406.06608
1.1What is a Prompt?A prompt is an input to a Generative AI model, that is used to guide its output. Prompts may consist of text, image, sound, or other media. Some examples of prompts include: “write a three paragraph email for a marketing campaign for an accounting firm”, a photograph of a table accompanied by the text “describe everything on the table”, or a recording of an online meeting, with the instructions “summarize this”.Prompt TemplatePrompts are often constructed via a prompt template. A prompt template is a function that contains one or more variables which will be replaced by some media (usually text)  to create a prompt. This prompt can then be considered to be aninstanceof the template.Consider applying prompting to the task of binary classification of tweets. Here is an initial prompt template that can be used to classify inputs.Classify the tweet as positive or negative:{TWEET}Each tweet in the dataset would be inserted into a separate instance of the template and the resulting prompt would be given to a LLM for inference.Write a poem about trees.Write a poem about the following topic: {USER_INPUT}Figure 1.2:Prompts and prompt templates are distinct concepts; a prompt template becomes a prompt when input is inserted into it.
introduction
The Prompt Report: A Systematic Survey of Prompting Techniques
Section 1
2406.06608
1.2Terminology{forest}for tree=
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.Figure 1.3:A Terminology of prompting. Terms with links to the appendix are not sufficiently critical to describe in the main paper, but are important to the field of prompting. Prompting techniques are shown in Figure2.21.2.1Components of a PromptThere are a variety of common components included in a prompt. We summarize the most commonly used components and discuss how they fit into prompts.DirectiveMany prompts issue a directive in the form of an instruction or question.111“Directives”, from, are a type of speech act intended to encourage an action, and have been invoked in models of human-computer dialogue.This is the core intent of the prompt, sometimes simply called the "intent". For example, here is an example of a prompt with a single instruction:Tell me five good books to read.Directives can also be implicit, as in this one-shot case, where the directive is to perform English to Spanish translation:Night: NocheMorning:ExamplesExamples, also known as exemplars or shots, act as demonstrations that guide the GenAI to accomplish a task. The above prompt is a One-Shot (i.e. one example)  prompt.Output FormattingIt is often desirable for the GenAI to output information in certain formats, for example, CSVs or markdown formats. To facilitate this, you can simply add instructions to do so as seen below:{PARAGRAPH}Summarize this into a CSV.Style InstructionsStyle instructions are a type of output formatting used to modify the output stylistically rather than structurally (Section2.2.2) . For example:Write a clear and curt paragraph about llamas.RoleA Role, also known as a persona, is a frequently discussed component that can improve writing and style text (Section2.2.2) . For example:Pretend you are a shepherd and write a limerick about llamas.Additional InformationIt is often necessary to include additional information in the prompt. For example, if the directive is to write an email, you might include information such as your name and position so the GenAI can properly sign the email. Additional Information is sometimes called ‘context‘, though we discourage the use of this term as it is overloaded with other meanings in the prompting space222e.g. the context is the tokens processed by the LLM in a forward pass.1.2.2Prompting TermsTerminology within the prompting literature is rapidly developing. As it stands, there are many poorly understood definitions (e.g. prompt, prompt engineering)  and conflicting ones (e.g. role prompt vs persona prompt) . The lack of a consistent vocabulary hampers the community’s ability to clearly describe the various prompting techniques in use. We provide a robust vocabulary of terms used in the prompting community (Figure1.3) .333By robust, we mean that it covers most existing commonly used terms in the field.Less frequent terms are left to AppendixA.2. In order to accurately define frequently-used terms like prompt and prompt engineering, we integrate many definitions (AppendixA.1)  to derive representative definitions.PromptingPrompting is the process of providing a prompt to a GenAI, which then generates a response. For example, the action of sending a chunk of text or uploading an image constitutes prompting.Prompt ChainA prompt chain (activity: prompt chaining)  consists of two or more prompt templates used in succession. The output of the prompt generated by the first prompt template is used to parameterize the second template, continuing until all templates are exhausted.Prompting TechniqueA prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multiple prompts. A prompting technique may incorporate conditional or branching logic, parallelism, or other architectural considerations spanning multiple prompts.Prompt EngineeringPrompt engineering is the iterative process of developing a prompt by modifying or changing the prompting technique that you are using (Figure1.4) .Figure 1.4:The Prompt Engineering Process consists of three repeated steps 1)  performing inference on a dataset 2)  evaluating performance and 3)  modifying the prompt template. Note that the extractor is used to extract a final response from the LLM output (e.g. "This phrase is positive""positive") . See more information on extractors in Section2.5.Prompt Engineering TechniqueA prompt engineering technique is a strategy for iterating on a prompt to improve it. In literature, this will often be automated techniques, but in consumer settings, users often perform prompt engineering manually.ExemplarExemplars are examples of a task being completed that are shown to a model in a prompt.
body
The Prompt Report: A Systematic Survey of Prompting Techniques
Section 2
2406.06608
1.3A Short History of PromptsThe idea of using natural language prefixes, or prompts, to elicit language model behaviors and responses originated before the GPT-3 and ChatGPT era. GPT-2makes use of prompts and they appear to be first used in the context of Generative AI by. However, the concept of prompts was preceded by related concepts such as control codesand writing prompts.The term Prompt Engineering appears to have come into existence more recently fromthen slightly later from.However, various papers perform prompt engineering without naming the term, includingfor non-autoregressive language models.Some of the first works on prompting define a prompt slightly differently to how it is currently used. For example, consider the following prompt from:Translate English to French:llamaconsider the word "llama" to be the prompt, while "Translate English to French:" is the "task description". More recent papers, including this one, refer to the entire string passed to the LLM as the prompt.
body
The Prompt Report: A Systematic Survey of Prompting Techniques
Section 3
2406.06608
2.1Systematic Review ProcessIn order to robustly collect a dataset of sources for this paper, we ran a systematic literature review grounded in the PRISMA process(Figure2.1) . We host this dataset onHuggingFaceand present a datasheetfor the dataset in AppendixA.3. Our main data sources werearXiv,Semantic Scholar, andACL. We query these databases with a list of 44 keywords narrowly related to prompting and prompt engineering (AppendixA.4) .2.1.1The PipelineIn this section, we introduce our data scraping pipeline, which includes both human and LLM-assisted review.444UsingGPT-4-1106-previewAs an initial sample to establish filtering critera, we retrieve papers from arXiv based on a simple set of keywords and boolean rules (A.4) .
Then, human annotators label a sample of 1,661 articles from the arXiv set for the following criteria:1.Does the paper propose a novel prompting technique? (include) 2.Does the paper strictly cover hard prefix prompts? (include) 3.Does the paper focus on training by backpropagating gradients? (exclude) 4.For non-text modalities, does it use a masked frame and/or window? (include) A set of 300 articles are reviewed independently by two annotators, with 92% agreement (Krippendorff’s= Cohen’s= 81%) .
Next, we develop a prompt usingGPT-4-1106-previewto classify the remaining articles. We validate the prompt against 100 ground-truth annotations, achieving 89% precision and 75% recall (for anof 81%) .
The combined human and LLM annotations generate a final set of 1,565 papers.Figure 2.1:The PRISMA review process. We accumulate 4,247 unique records from which we extract 1,565 relevant records.
body
The Prompt Report: A Systematic Survey of Prompting Techniques
Section 4
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2.2Text-Based TechniquesWe now present a comprehensive taxonomical ontology of 58 text-based prompting techniques, broken into 6 major categories (Figure2.2) . Although some of the techniques might fit into multiple categories, we place them in a single category of most relevance.{forest}for tree=
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]Figure 2.2:All text-based prompting techniques from our dataset.2.2.1In-Context Learning (ICL) Figure 2.3:We highlight six main design decisions when crafting few-shot prompts.∗Please note that recommendations heredo notgeneralize to all tasks; in some cases, each of them could hurt performance.ICL refers to the ability of GenAIs to learn skills and tasks by providing them with exemplars and or relevant instructions within the prompt, without the need for weight updates/retraining. These skills can be learned from exemplars (Figure2.4)  and/or instructions (Figure2.5) . Note that the word ’learn’ is misleading. ICL can simply be task specification–the skills are not necessarily new, and can have already been included in the training data (Figure2.6) . See AppendixA.8for a discussion of the use of this term. Significant work is currently being done on optimizingand understandingICL.2+2: four4+5: nine8+0:Figure 2.4:ICL exemplar promptExtract all words that have 3 of the same letter and at least 3 other letter from the following text: {TEXT}Figure 2.5:ICL instruction promptTranslate the word "cheese" to French.Figure 2.6:ICL from training data prompt. In this version of ICL, the model is not learning a new skill, but rather using knowledge likely in its training set.Few-Shot Promptingis the paradigm seen in Figure2.4, where the GenAI learns to complete a task with only a few examples (exemplars) .Few-Shot Learning (FSL) is often conflated with Few-Shot Prompting. It is important to note that FSL is a broader machine learning paradigm to adapt parameters with a few examples, while Few-Shot Prompting is specific to prompts in the GenAI settings and does not involve updating model parameters.2.2.1.1Few-Shot Prompting Design DecisionsSelecting exemplars for a prompt is a difficult task–performance depends significantly on various factors of the exemplars, and only a limited number of exemplars fit in the typical LLM’s context window. We highlight six separate design decisions, including the selection and order of exemplars that critically influence the output quality(Figure2.3) .Exemplar QuantityIncreasing the quantity of exemplars in the prompt generally improves model performance, particularly in larger models. However, in some cases, the benefits may diminish beyond 20 exemplars.Exemplar OrderingThe order of exemplars affects model behavior. On some tasks, exemplar order can cause accuracy to vary from sub-50% to 90%+.Exemplar Label DistributionAs in traditional supervised machine learning, the distribution of exemplar labels in the prompt affects behavior. For example, if 10 exemplars from one class and 2 exemplars of another class are included, this may cause the model to be biased toward the first class.Exemplar Label QualityDespite the general benefit of multiple exemplars, the necessity of strictlyvaliddemonstrations is unclear. Some worksuggests that the accuracy of labels is irrelevant—providing models with exemplars with incorrect labels may not negatively diminish performance.
However, under certain settings, there is a significant impact on performance. Larger models are often better at handling incorrect or unrelated labels.It is important to discuss this factor, since if you are automatically constructing prompts from large datasets that may contain inaccuracies, it may be necessary to study how label quality affects your results.Exemplar FormatThe formatting of exemplars also affects performance. One of the most common formats is “Q: {input}, A: {label}”, but the optimal format may vary across tasks; it may be worth trying multiple formats to see which performs best. There is some evidence to suggest that formats that occur commonly in the training data will lead to better performance.Exemplar SimilaritySelecting exemplars that are similar to the test sample is generally beneficial for performance. However, in some cases, selecting more diverse exemplars can improve performance.2.2.1.2Few-Shot Prompting TechniquesConsidering all of these factors, Few-Shot Prompting can be very difficult to implement effectively. We now examine techniques for Few-Shot Prompting in the supervised setting. Ensembling approaches can also benefit Few-Shot Prompting, but we discuss them separately (Section2.2.5) .Assume we have a training dataset,, which contains multiple inputsand outputs, which can be used to few-shot prompt a GenAI (rather than performing gradient-based updates) . Assume that this prompt can be dynamically generated with respect toat test time. Here is the prompt template we will use for this section, following the ‘input: output‘ format (Figure2.4) :{Exemplars}:Figure 2.7:Few-Shot Prompting TemplateK-Nearest Neighbor (KNN) is part of a family of algorithms that selects exemplars similar toto boost performance. Although effective, employing KNN during prompt generation may be time and resource intensive.Vote-Kis another method to select similar exemplars to the test sample. In one stage, a model proposes useful unlabeled candidate exemplars for an annotator to label. In the second stage, the labeled pool is used for Few-Shot Prompting. Vote-K also ensures that newly added exemplars are sufficiently different than existing ones to increase diversity and representativeness.Self-Generated In-Context Learning (SG-ICL) leverages a GenAI to automatically generate exemplars. While better than zero-shot scenarios when training data is unavailable, the generated samples are not as effective as actual data.Prompt Miningis the process of
discovering optimal "middle words" in prompts (effectively prompt templates)  through large corpus analysis. For example, instead of using the common "Q: A:" format for few-shot prompts, there may exist something similar which occurs more frequently in the corpus. Formats which occur more often in the corpus will likely lead to improved prompt performance.More Complicated Techniquessuch as LENS, UDR, and Active Example Selectionleverage iterative filtering, embedding and retrieval, and reinforcement learning, respectively.2.2.2Zero-ShotIn contrast to Few-Shot Prompting, Zero-Shot Prompting uses zero exemplars. There are a number of well known standalone zero-shot techniques as well as zero-shot techniques combine with another concept (e.g. Chain of Thought) , which we discuss later (Section2.2.3) .Role Prompting, also known as persona prompting, assigns a specific role to the GenAI in the prompt. For example, the user might prompt it to act like "Madonna" or a "travel writer". This can create more desirable outputs for open-ended tasksand in some cases improve accuracy on benchmarks.Style Promptinginvolves specifying the desired style, tone, or genre in the prompt to shape the output of a GenAI. A similar effect can be achieved using role prompting.Emotion Promptingincorporates phrases of psychological relevance to humans (e.g., "This is important to my career")  into the prompt, which may lead to improved LLM performance on benchmarks and open-ended text generation.System 2 Attention (S2A) first asks an LLM to rewrite the prompt and remove any information unrelated to the question therein. Then, it passes this new prompt into an LLM to retrieve a final response.SimToMdeals with complicated questions which involve multiple people or objects. Given the question, it attempts to establish the set of facts one person knows, then answer the question based only on those facts. This is a two prompt process and can help eliminate the effect of irrelevant information in the prompt.Rephrase and Respond (RaR) instructs the LLM to rephrase and expand the question before generating the final answer. For example, it might add the following phrase to the question: "Rephrase and expand the question, and respond". This could all be done in a single pass or the new question could be passed to the LLM separately. RaR has demonstrated improvements on multiple benchmarks.Re-reading (RE2) adds the phrase "Read the question again:" to the prompt in addition to repeating the question. Although this is such a simple technique, it has shown improvement in reasoning benchmarks, especially with complex questions.Self-Askprompts LLMs to first decide if they need to ask follow up questions for a given prompt. If so, the LLM generates these questions, then answers them and finally answers the original question.2.2.3Thought GenerationThought generation encompasses a range of techniques that prompt the LLM to articulate its reasoning while solving a problem.Chain-of-Thought (CoT)  Promptingleverages few-shot prompting to encourage the LLM to express its thought process before delivering its final answer.555We note that such techniques are often described using words like ”think” that anthropomorphize models. We attempt not to use this language, but do use original authors’ language where appropriate.This technique is occasionally referred to as Chain-of-Thoughts. It has been demonstrated to significantly enhance the LLM’s performance in mathematics and reasoning tasks. In, the prompt includes an exemplar featuring a question, a reasoning path, and the correct answer (Figure2.8) .Q: Jack has two baskets, each containing three balls. How many balls does Jack have in total?A: One basket contains 3 balls, so two baskets contain 3 * 2 = 6 balls.Q: {QUESTION}A:Figure 2.8:A One-Shot Chain-of-Thought Prompt.2.2.3.1Zero-Shot-CoTThe most straightforward version of CoT contains zero exemplars. It involves appending a thought inducing phrase like "Let’s think step by step."to the prompt. Other suggested thought-generating phrases include "Let’s work this out in a step by step way to be sure we have the right answer"and "First, let’s think about this logically".searches for an optimal thought inducer. Zero-Shot-CoT approaches are attractive as they don’t require exemplars and are generally task agnostic.Step-Back Promptingis a modification of CoT where the LLM is first asked a generic, high-level question about relevant concepts or facts before delving into reasoning. This approach has improved performance significantly on multiple reasoning benchmarks for both PaLM-2L and GPT-4.Analogical Promptingis similar to SG-ICL, and automatically generates exemplars that include CoTs. It has demonstrated improvements in mathematical reasoning and code generation tasks.Thread-of-Thought (ThoT)  Promptingconsists of an improved thought inducer for CoT reasoning. Instead of "Let’s think step by step," it uses "Walk me through this context in manageable parts step by step, summarizing and analyzing as we go." This thought inducer works well in question-answering and retrieval settings, especially when dealing with large, complex contexts.Tabular Chain-of-Thought (Tab-CoT) consists of a Zero-Shot CoT prompt that makes the LLM output reasoning as a markdown table. This tabular design enables the LLM to improve the structure and thus the reasoning of its output.2.2.3.2Few-Shot CoTThis set of techniques present the LLM with multiple exemplars, which include chains-of-thought. This can significantly enhance performance. This technique is occasionally referred to as Manual-CoTor Golden CoT.Contrastive CoT Promptingadds both exemplars with incorrect and correct explanations to the CoT prompt in order to show the LLM hownotto reason. This method has shown significant improvement in areas like Arithmetic Reasoning and Factual QA.Uncertainty-Routed CoT Promptingsamples multiple CoT reasoning paths, then selects the majority if it is above a certain threshold (calculated based on validation data) . If not, it samples greedily and selects that response. This method demonstrates improvement on the MMLU benchmark for both GPT4 and Gemini Ultra models.Complexity-based Promptinginvolves two major modifications to CoT. First, it selects complex examples for annotation and inclusion in the prompt, based on factors like question length or reasoning steps required. Second, during inference, it samples multiple reasoning chains (answers)  and uses a majority vote among chains exceeding a certain length threshold, under the premise that longer reasoning indicates higher answer quality. This technique has shown improvements on three mathematical reasoning datasets.Active Promptingstarts with some training questions/exemplars, asks the LLM to solve them, then calculates uncertainty (disagreement in this case)  and asks human annotators to rewrite the exemplars with highest uncertainty.Memory-of-Thought Promptingleverage unlabeled training exemplars to build Few-Shot CoT prompts at test time. Before test time, it performs inference on the unlabeled training exemplars with CoT. At test time, it retrieves similar instances to the test sample. This technique has shown substantial improvements in benchmarks like Arithmetic, commonsense, and factual reasoning.Automatic Chain-of-Thought (Auto-CoT)  Promptinguses’s Zero-Shot prompt to automatically generate chains of thought. These are then used to build a Few-Shot CoT prompt for a test sample.2.2.4DecompositionSignificant research has focused on decomposing complex problems into simpler sub-questions. This is an effective problem-solving strategy for humans as well as GenAI. Some decomposition techniques are similar to thought-inducing techniques, such as CoT, which often naturally breaks down problems into simpler components. However, explicitly breaking down problems can further improve LLMs’ problem solving ability.Least-to-Most Promptingstarts by prompting a LLM to break a given problem into sub-problems without solving them. Then, it solves them sequentially, appending model responses to the prompt each time, until it arrives at a final result. This method has shown significant improvements in tasks involving symbolic manipulation, compositional generalization, and mathematical reasoning.Decomposed Prompting (DECOMP) Few-Shot prompts a LLM to show it how to use certain functions. These might include things like string splitting or internet searching; these are often implemented as separate LLM calls. Given this, the LLM breaks down its original problem into sub-problems which it sends to different functions. It has shown improved performance over Least-to-Most prompting on some tasks.Plan-and-Solve Promptingconsists of an improved Zero-Shot CoT prompt, "Let’s first understand the problem and devise a plan to solve it. Then, let’s carry out the plan and solve the problem step by step". This method generates more robust reasoning processes than standard Zero-Shot-CoT on multiple reasoning datasets.Tree-of-Thought (ToT) , also known as Tree of Thoughts,, creates a tree-like search problem by starting with an initial problem then generating multiple possible steps in the form of thoughts (as from a CoT) . It evaluates the progress each step makes towards solving the problem (through prompting)  and decides which steps to continue with, then keeps creating more thoughts. ToT is particularly effective for tasks that require search and planning.Recursion-of-Thoughtis similar to regular CoT. However, every time it encounters a complicated problem in the middle of its reasoning chain, it sends this problem into another prompt/LLM call. After this is completed, the answer is inserted into the original prompt. In this way, it can recursively solve complex problems, including ones which might otherwise run over that maximum context length. This method has shown improvements on arithmetic and algorithmic tasks. Though implemented using fine-tuning to output a special token that sends sub-problem into another prompt, it could also be done only through prompting.Program-of-Thoughtsuses LLMs like Codex to generate programming code as reasoning steps. A code interpreter executes these steps to obtain the final answer. It excels in mathematical and programming-related tasks but is less effective for semantic reasoning tasks.Faithful Chain-of-Thoughtgenerates a CoT that has both natural language and symbolic language (e.g. Python)  reasoning, just like Program-of-Thoughts. However, it also makes use of different types of symbolic languages in a task-dependent fashion.Skeleton-of-Thoughtfocuses on accelerating answer speed through parallelization. Given a problem, it prompts an LLM to create a skeleton of the answer, in a sense, sub-problems to be solved. Then, in parallel, it sends these questions to an LLM and concatenates all the outputs to get a final response.2.2.5EnsemblingIn GenAI, ensembling is the process of using multiple prompts to solve the same problem, then aggregating these responses into a final output. In many cases, a majority vote—selecting the most frequent response—is used to generate the final output. Ensembling techniques reduce the variance of LLM outputs and often improving accuracy, but come with the cost of increasing the number of model calls needed to reach a final answer.Demonstration Ensembling (DENSE) creates multiple few-shot prompts, each containing a distinct subset of exemplars from the training set. Next, it aggregates over their outputs to generate a final response.Mixture of Reasoning Experts (MoRE) creates a set of diverse reasoning experts by using different specialized prompts for different reasoning types (such as retrieval augmentation prompts for factual reasoning, Chain-of-Thought reasoning for multi-hop and math reasoning, and generated knowledge prompting for commonsense reasoning) . The best answer from all experts is selected based on an agreement score.Max Mutual Information Methodcreates multiple prompt templates with varied styles and exemplars, then selects the optimal template as the one that maximizes mutual information between the prompt and the LLM’s outputs.Self-Consistencyis based on the intuition that multiple different reasoning paths can lead to the same answer. This method first prompts the LLM multiple times to perform CoT, crucially with a non-zero temperature to elicit diverse reasoning paths. Next, it uses a majority vote over all generated responses to select a final response. Self-Consistency has shown improvements on arithmetic, commonsense, and symbolic reasoning tasks.Universal Self-Consistencyis similar to Self-Consistency except that rather that selecting the majority response by programmatically counting how often it occurs, it inserts all outputs into a prompt template that selects the majority answer. This is helpful for free-form text generation and cases where the same answer may be output slightly differently by different prompts.Meta-Reasoning over Multiple CoTsis similar to universal Self-Consistency; it first generates multiple reasoning chains (but not necessarily final answers)  for a given problem. Next, it inserts all of these chains in a single prompt template then generates a final answer from them.DiVeRSecreates multiple prompts for a given problem then performs Self-Consistency for each, generating multiple reasoning paths. They score reasoning paths based on each step in them then select a final response.Consistency-based Self-adaptive Prompting (COSP) constructs Few-Shot CoT prompts by running Zero-Shot CoT with Self-Consistency on a set of examples then selecting a high agreement subset of the outputs to be included in the final prompt as exemplars. It again performs Self-Consistency with this final prompt.Universal Self-Adaptive Prompting (USP) builds upon the success of COSP, aiming to make it generalizable to all tasks. USP makes use of unlabeled data to generate exemplars and a more complicated scoring function to select them. Additionally, USP does not use Self-Consistency.Prompt Paraphrasingtransforms an original prompt by changing some of the wording, while still maintaining the overall meaning. It is effectively a data augmentation technique that can be used to generate prompts for an ensemble.2.2.6Self-CriticismWhen creating GenAI systems, it can be useful to have LLMs criticize their own outputs. This could simply be a judgement (e.g., is this output correct)  or the LLM could be prompted to provide feedback, which is then used to improve the answer. Many approaches to generating and integrating self-criticism have been developed.Self-Calibrationfirst prompts an LLM to answer a question. Then, it builds a new prompt that includes the question, the LLM’s answer, and an additional instruction asking whether the answer is correct. This can be useful for gauging confidence levels when applying LLMs when deciding when to accept or revise the original answer.Self-Refineis an iterative framework where, given an initial answer from the LLM, it prompts the same LLM to provide feedback on the answer, and then prompts the LLM to improve the answer based on the feedback.
This iterative process continues until a stopping condition is met (e.g., max number of steps reached) . Self-Refine has demonstrated improvement across a range of reasoning, coding, and generation tasks.Reversing Chain-of-Thought (RCoT) first prompts LLMs to reconstruct the problem based on
generated answer. Then, it generates fine-grained comparisons between the original problem
and the reconstructed problem as a way to check for any inconsistencies.
These inconsistencies are then converted to feedback for the LLM to revise the generated answer.Self-Verificationgenerates multiple candidate solutions with Chain-of-Thought (CoT) . It then scores each solution by masking certain parts of the original question and asking an LLM to predict them based on the rest of the question and the generated solution. This method has shown improvement on eight reasoning datasets.Chain-of-Verification (COVE) first uses an LLM to generate an answer to a given question. Then creates a list of related questions that would help verify the correctness of the answer. Each question is answered by the LLM, then all the information is given to the LLM to produce the final revised answer. This method has shown improvements in various question-answering and text-generation tasks.Cumulative Reasoningfirst generates several potential steps in answering the question. It then has a LLM evaluate them, deciding to either accept or reject these steps. Finally, it checks whether it has arrived at the final answer. If so, it terminates the process, but otherwise it repeats it. This method has demonstrated improvements in logical inference tasks and mathematical problem.
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2.3Prompting Technique UsageFigure 2.9:Citation Counts of GenAI ModelsFigure 2.10:Citation Counts of DatasetsFigure 2.11:Citation Counts of Prompting Techniques. The top 25 papers in our dataset, measured by how often they are cited by other papers in our dataset. Most papers here are prompting techniques*, and the remaining papers contains prompting advice.As we have just seen, there exist many text-based prompting techniques. However, only a small subset of them are commonly used in research and in industry. We measure technique usage by proxy of measuring the number of citations by other papers in our dataset. We do so with the presumption that papers about prompting are more likely to actually use or evaluate the cited technique. We graph the top 25 papers cited in this way from our dataset and find that most of them propose new prompting techniques (Figure2.11) . The prevalence of citations for Few-Shot and Chain-of-Thought prompting is unsurprising and helps to establish a baseline for understanding the prevalence of other techniques.2.3.1BenchmarksIn prompting research, when researchers propose a new technique, they usually benchmark it across multiple models and datasets. This is important to prove the utility of the technique and examine how it transfers across models.In order to make it easier for researchers proposing new techniques to know how to benchmark them, we quantitatively examine which models (Figure2.11)  and what benchmark datasets (Figure2.11)  are being used. Again, we measure usage by how many times papers in our dataset cite the benchmark datasets and models.To find which datasets and models are being used, we prompted GPT-4-1106-preview to extract any mentioned dataset or model from the body of papers in our dataset. After, we manually filtered out results that were not models or datasets. The citation counts were acquired by searching items from the finalized list on Semantic Scholar.
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2.4Prompt EngineeringIn addition to surveying prompting technique, we also review prompt engineering techniques, which are used to automatically optimize prompts. We discuss some techniques that use gradient updates, since the set of prompt engineering techniques is much smaller than that of prompting techniques.Meta Promptingis the process of prompting a LLM to generate or improve a prompt or prompt template.AutoPromptuses a frozen LLM as well as a prompt template that includes some "trigger tokens", whose values are updated via backpropogation at training time. This is a version of soft-prompting.Automatic Prompt Engineer (APE) uses a set of exemplars to generate a Zero-Shot instruction prompt. It generates multiple possible prompts, scores them, then creates variations of the best ones (e.g. by using prompt paraphrasing) . It iterates on this process until some desiderata are reached.Gradientfree Instructional Prompt Search (GrIPS) is similar to APE, but uses a more complex set of operations including deletion, addition, swapping, and paraphrasing in order to create variations of a starting prompt.Prompt Optimization with Textual Gradients (ProTeGi) is a unique approach to prompt engineering that improves a prompt template through a multi-step process. First, it passes a batch of inputs through the template, then passes the output, ground truth, and prompt into another prompt that criticizes the original prompt. It generates new prompts from these criticisms then uses a bandit algorithmto select one. ProTeGi demonstrates improvements over methods like APE and GRIPS.RLPromptuses a frozen LLM with an unfrozen module added. It uses this LLM to generate prompt templates, scores the templates on a dataset, and updates the unfrozen module using Soft Q-Learning. Interestingly, the method often selects grammatically gibberish text as the optimal prompt template.Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization (DP2O) is perhaps the most complicated prompt engineering technique, involving reinforcement learning, a custom prompt scoring function, and conversations the a LLM in order to construct the prompt.
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2.5Answer EngineeringAnswer engineering is the iterative process of developing or selecting among algorithms that extract precise answers from LLM outputs. To understand the need for answer engineering, consider a binary classification task where the labels are "Hate Speech" and "Not Hate Speech". The prompt template might look like this:Is this "Hate Speech" or "Not Hate Speech": {TEXT}When a hate speech sample is put through the template, it might have outputs such as "It’s hate speech", "Hate Speech.", or even "Hate speech, because it uses negative language against a racial group". This variance in response formats is difficult to parse consistently; improved prompting can help, but only to a certain extent.There are three design decisions in answer engineering, the choice of answer space, answer shape, and answer extractor (Figure2.12) .define the first two as necessary components of answer engineering and we append the third. We consider answer engineering to be distinct from prompt engineering, but extremely closely related; the processes are often conducted in tandem.Figure 2.12:An annotated output of a LLM output for a labeling task, which shows the three design decisions of answer engineering: the choice of answer shape, space, and extractor. Since this is an output from a classification task, the answer shape could be restricted to a single token and the answer space to one of two tokens ("positive" or "negative") , though they are unrestricted in this image.2.5.1Answer ShapeThe shape of an answer is its physical format. For example, it could be a token, span of tokens, or even an image or video.666We use a different definition thanwith respect to granularity (e.g. token vs span) , since the output could be of a different modality.It is sometimes useful to restrict the output shape of a LLM to a single token for tasks like binary classification.2.5.2Answer SpaceThe space of an answer is the domain of values that its structure may contain. This may simply be the space of all tokens, or in a binary labeling task, could just be two possible tokens.2.5.3Answer ExtractorIn cases where it is impossible to entirely control the answer space (e.g. consumer-facing LLMs) , or the expected answer may be located somewhere within the model output, a rule can be defined to extract the final answer. This rule is often a simple function (e.g. a regular expression) , but can also use a separate LLM to extract the answer.VerbalizerOften used in labeling tasks, a verbalizer maps a token, span, or other type of output to a label and vice-versa (injective) . For example, if we wish for a model to predict whether a Tweet is positive or negative, we could prompt it to output either "+" or "-" and a verbalizer would map these token sequences to the appropriate labels. The selection of a verbalizer constitutes a component of answer engineering.RegexAs mentioned previously, Regexes are often used to extract answers. They are usually used to search for the first instance of a label. However, depending on the output format and whether CoTs are generated, it may be better to search for the last instance.Separate LLMSometimes outputs are so complicated that regexes won’t work consistently. In this case, it can be useful to have a separate LLM evaluate the output and extract an answer.
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3.1MultilingualState-of-the-art GenAIs have often been predominately trained with English dataset, leading to a notable disparity in the output quality in languages other than English, particularly low-resource languages.
As a result, various multilingual prompting techniques have emerged in an attempt to improve model performance in non-English settings.Translate First Promptingis perhaps the simplest strategy and first translates non-English input examples into English. By translating the inputs into English, the model can utilize its strengths in English to better understand the content. Translation tools vary;use an external MT system,prompt multilingual LMs andprompt LLMs to translate non-English inputs.3.1.1Chain-of-Thought (CoT) CoT promptinghas been extended to the multilingual setting in multiple ways.XLT (Cross-Lingual Thought)  Promptingutilizes a prompt template composed of six separate instructions, including role assignment, cross-lingual thinking, and CoT.Cross-Lingual Self Consistent Prompting (CLSP) introduces an ensemble technique that constructs reasoning paths in different languages to answer the same question.3.1.2In-Context LearningICL has also been extended to multilingual settings in multiple ways.X-InSTA Promptingexplores three distinct approaches for aligning in-context examples with the input sentence for classification tasks: using semantically similar examples to the input (semantic alignment) , examples that share the same label as the input (task-based alignment) , and the combination of both semantic and task-based alignments.In-CLT (Cross-lingual Transfer)  Promptingleverages both the source and target languages to create in-context examples, diverging from the traditional method of using source language exemplars. This strategy helps stimulate the cross-lingual cognitive capabilities of multilingual LLMs, thus boosting performance on cross-lingual tasks.3.1.3In-Context Example SelectionIn-context example selection heavily influences the multilingual performance of LLMs. Finding in-context examples that are semantically similar to the source text is very important. However, using semantically dissimilar (peculiar)  exemplars has also been shown to enhance performance. This same contrast exists in the English-only setting. Additionally, when dealing with ambiguous sentences, selecting exemplars with polysemous or rare word senses may boost performance.PARC (Prompts Augmented by Retrieval Cross-lingually) introduce a framework that retrieves relevant exemplars from a high resource language. This framework is specifically designed to enhance cross-lingual transfer performance, particularly for low-resource target languages.extend this work to Bangla.3.1.4Prompt Template Language SelectionIn multilingual prompting, the selection of language for the prompt template can markedly influence the model performance.English Prompt TemplateConstructing the prompt template in English is often more effective than in the task language for multilingual tasks. This is likely due to the predominance of English data during LLM pre-training.suggest that this is likely due to a high overlap with pre-training data and vocabulary. Similarly,highlight how translation errors when creating task language templates propagate in the form of incorrect syntax and semantics, adversely affecting task performance. Further,compare in-lingual (task language)  prompts and cross-lingual (mixed language)  prompts and find the cross-lingual approach to be more effective, likely because it uses more English in the prompt, thus facilitating retrieving knowledge from the model.Task Language Prompt TemplateIn contrast, many multilingual prompting benchmarks such as BUFFETor LongBenchuse task language prompts for language-specific use cases.specifically studies different translation methods when constructing native-language prompts. They demonstrate that human translated prompts are superior to their machine-translated counterparts. Native or non-native template performance can differ across tasks and models. As such, neither option will always be the best approach.3.1.5Prompting for Machine TranslationThere is significant research into leveraging GenAI to facilitate accurate and nuanced translation. Although this is a specific application of prompting, many of these techniques are important more broadly for multilingual prompting.Multi-Aspect Prompting and Selection (MAPS) mimics the human translation process, which involves multiple preparatory steps to ensure high-quality output. This framework starts with knowledge mining from the source sentence (extracting keywords and topics, and generating translation exemplars) . It integrates this knowledge to generate multiple possible translations, then selects the best one.Chain-of-Dictionary (CoD) first extracts words from the source phrase, then makes a list of their meanings in multiple languages, automatically via retrieval from a dictionary (e.g. English: ‘apple’, Spanish: ‘manzana’) . Then, they prepend these dictionary phrases to the prompt, where it asks a GenAI to use them during translation.Dictionary-based Prompting for Machine Translation (DiPMT) works similarly to CoD, but only gives definitions in the source and target languages, and formats them slightly differently.Decomposed Prompting for MT (DecoMT) divides the source text into several chunks and translates them independently using few-shot prompting. Then it uses these translations and contextual information between chunks to generate a final translation.3.1.5.1Human-in-the-LoopInteractive-Chain-Prompting (ICP) deals with potential ambiguities in translation by first asking the GenAI to generate sub-questions about any ambiguities in the phrase to be translated. Humans later respond to these questions and the system includes this information to generate a final translation.Iterative Promptingalso involves humans during translation. First, they prompt LLMs to create a draft translation. This initial version is further refined by integrating supervision signals obtained from either automated retrieval systems or direct human feedback.{forest}for tree=
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3.2MultimodalAs GenAI models evolve beyond text-based domains, new prompting techniques emerge. These multimodal prompting technique are often not simply applications of text-based prompting techniques, but entirely novel ideas made possible by different modalities. We now extend our text-based taxonomy to include a mixture of multimodal analogs of text-based prompting techniques as well as completely novel multimodal techniques.3.2.1Image PromptingThe image modality encompasses data such as photographs, drawings, or even screenshots of text. Image prompting may refer to prompts that either contain images or are used to generate images. Common tasks include image generation, caption generation, image classification, and image editing. We now describe various image prompting techniques used for such applications.Prompt Modifiersare simply words appended to a prompt to change the resultant image. Components such as Medium (e.g. "on canvas")  or Lighting (e.g. "a well lit scene")  are often used.Negative Promptingallows users to numerically weight certain terms in the prompt so that the model considers them more/less heavily than others. For example, by negatively weighting the terms “bad hands” and “extra digits”, models may be more likely to generate anatomically accurate hands.3.2.1.1Multimodal In-Context LearningThe success of ICL in text-based settings has prompted research into multimodal ICL.Paired-Image Promptingshows the model two images: one before and one after some transformation. Then, present the model with a new image for which it will perform the demonstrated conversion. This can be done either with textual instructionsor without them.Image-as-Text Promptinggenerates a textual description of an image. This allows for the easy inclusion of the image (or multiple images)  in a text-based prompt.3.2.1.2Multimodal Chain-of-ThoughtCoT has been extended to the image domain in various ways. A simple example of this would be a prompt containing an image of a math problem accompanied by the textual instructions "Solve this step by step".Duty Distinct Chain-of-Thought (DDCoT) extends Least-to-Most promptingto the multimodal setting, creating subquestions, then solving them and combining the answers into a final response.Multimodal Graph-of-Thoughtextends Graph-of-Thoughtto the multimodal setting. GoT-Input also uses a two step rationale then answer process. At inference time, the the input prompt is used to construct a thought graph, which is then used along with the original prompt to generate a rationale to answer the question. When an image is input along with the question, an image captioning model is employed to generate a textual description of the image, which is then appended to the prompt before the thought graph construction to provide visual context.Chain-of-Images (CoI) is a multimodal extension of Chain-of-Thought prompting, that generates images as part of its thought process. They use the prompt “Let’s think image by image” to generate SVGs, which the model can then use to reason visually.3.2.2Audio PromptingPrompting has also been extended to the audio modality. Experiments with audio ICL have generated mixed results, with some open source audio models failing to perform ICL. However, other results do show an ICL ability in audio models. Audio prompting is currently in early stages, but we expect to see various prompting techniques proposed in the future.3.2.3Video PromptingPrompting has also been extended to the video modality, for use in text-to-video generation, video editing, and video-to-text generation.3.2.3.1Video Generation TechniquesWhen prompting a model to generate video, various modalities of prompts can be used as input, and several prompt-related techniques are often employed to enhance video generation. Image related techniques, such as prompt modifiers can often be used for video generation.3.2.4Segmentation PromptingPrompting can also be used for segmentation (e.g. semantic segmentation) .3.2.53D PromptingPrompting can also be used in 3D modalities, for example in 3D object synthesis, 3D surface texturing, and 4D scene generation (animating a 3D scene) , where input prompt modalities include text, image, user annotation (bounding boxes, points, lines) , and 3D objects.
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4.1AgentsAs LLMs have improved rapidly in capabilities, companiesand researchershave explored how to allow them to make use of external systems. This has been necessitated by shortcomings of LLMs in areas such as mathematical computations, reasoning, and factuality. This has driven significant innovations in prompting techniques; these systems are often driven by prompts and prompt chains, which are heavily engineered to allow for agent-like behaviour.Definition of AgentIn the context of GenAI, we define agents to be GenAI systems that serve a user’s goals via actions that engage with systems outside the GenAI itself.777We do not cover the notion of independently-acting AI, i.e. systems that in any sense have their own goalsThis GenAI is usually a LLM. As a simple example, consider an LLM that is tasked with solving the following math problem:If Annie has 4,939 grapes, and gives exactly 39% of them to Amy, how many does she have left?If properly prompted, the LLM could output the string CALC(4,939*.39) . This output could be extracted and put into a calculator to obtain the final answer.This is an example of an agent: the LLM outputs text which then uses a downstream tool. Agent LLMs may involve a single external system (as above) , or they may need to solve the problem ofrouting, to choose which external system to use.
Such systems also frequently involve memory and planning in addition to actions.Examples of agents include LLMs that can make API calls to use external tools like a calculator, LLMs that can output strings that cause actions to be taken in a gym-likeenvironment, and more broadly, LLMs which write and record plans, write and run code, search the internet, and more. OpenAI Assistants, LangChain Agents, and LlamaIndex Agentsare additional examples.4.1.1Tool Use AgentsTool use is a critical component for GenAI agents. Both symbolic (e.g. calculator, code interpreter)  and neural (e.g. a separate LLM)  external tools are commonly used. Tools may occasionally be referred to as expertsor modules.Modular Reasoning, Knowledge, and Language (MRKL)  Systemis one of the simplest formulations of an agent. It contains a LLM router providing access to multiple tools. The router can make multiple calls to get information such as weather or the current date. It then combines this information to generate a final response. Toolformer, Gorilla, Act-1, and othersall propose similar techniques, most of which involve some fine-tuning.Self-Correcting with Tool-Interactive Critiquing (CRITIC) first generates
a response to the prompt, with no external calls. Then, the same LLM criticizes this response for possible errors. Finally, it uses tools (e.g. Internet search or a code interpreter)  accordingly to verify or amend parts of the response.4.1.2Code-Generation AgentsWriting and executing code is another important ability of many agents.888This ability may be considered a tool (i.e. code interpreter) Program-aided Language Model (PAL) translates a problem directly into code, which is sent to a Python interpreter to generate an answer.Tool-Integrated Reasoning Agent (ToRA) is similar to PAL, but instead of a single code generation step, it interleaves code and reasoning steps for as long as necessary to solve the problem.TaskWeaveris also similar to PAL, transforming user requests into code, but can also make use of user-defined plugin.4.1.3Observation-Based AgentsSome agents are designed to solve problems by interacting with toy environments. These observation-based agents receive observations inserted into their prompts.Reasoning and Acting (ReAct) ()  generates a thought, takes an action, and receives an observation (and repeats this process)  when given a problem to solve. All of this information is inserted into the prompt so it has a memory of past thoughts, actions, and observations.Reflexionbuilds on ReAct, adding a layer of introspection. It obtains a trajectory of actions and observations, then is given an evaluation of success/failure. Then, it generates a reflection on what it did and what went wrong. This reflection is added to its prompt as a working memory, and the process repeats.4.1.3.1Lifelong Learning AgentsWork on LLM-integrated Minecraft agents has generated impressive results, with agents able to acquire new skills as they navigate the world of this open-world videogame. We view these agents not merely as applications of agent techniques to Minecraft, but rather novel agent frameworks which can be explored in real world tasks that require lifelong learning.Voyageris composed of three parts. First, it proposes tasks for itself to complete in order to learn more about the world. Second, it generates code to execute these actions. Finally, it saves these actions to be retrieved later when useful, as part of a long-term memory system. This system could be applied to real world tasks where an agent needs to explore and interact with a tool or website (e.g. penetration testing, usability testing) .Ghost in the Minecraft (GITM) starts with an arbitrary goal, breaks it down into subgoals recursively, then iteratively plans and executes actions by producing structured text (e.g. "equip(sword) ")  rather than writing code. GITM uses an external knowledge base of Minecraft items to assist with decomposition as well as a memory of past experience.4.1.4Retrieval Augmented Generation (RAG) In the context of GenAI agents, RAG is a paradigm in which information is retrieved from an external source and inserted into the prompt. This can enhance performance in knowledge intensive tasks. When retrieval itself is used as an external tool, RAG systems are considered to be agents.Verify-and-Editimproves on self-consistency by generating multiple chains-of-thought, then selecting some to be edited. They do this by retrieving relevant (external)  information to the CoTs, and allowing the LLM to augment them accordingly.Demonstrate-Search-Predictfirst decomposes a question into sub-questions, then uses queries to solve them and combine their responses in a final answer. It uses few-shot prompting to decompose the problem and combine responses.Interleaved Retrieval guided by Chain-of-Thought (IRCoT) is a technique for multi-hop question answering that interleaves CoT and retrieval. IRCoT leverages CoT to guide which documents to retrieve and retrieval to help plan the reasoning steps of CoT.Iterative Retrieval Augmentationtechniques, like Forward-Looking Active REtrieval augmented
generation (FLARE) and Imitate, Retrieve, Paraphrase (IRP) , perform retrieval multiple times during long-form generation. Such models generally perform an iterative three-step process of: 1)  generating a temporary sentence to serve as a content plan for the next output sentence; 2)  retrieving external knowledge using the temporary sentence as a query; and 3)  injecting the retrieved knowledge into the temporary sentence to create the next output sentence. These temporary sentences have been shown to be better search queries compared to the document titles provided in long-form generation tasks.{forest}for tree=
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4.2EvaluationThe potential of LLMs to extract and reason about information and understand user intent makes them strong contenders as evaluators.999This section does not describe how to benchmark LLMs, but rather how to use them as evaluators.For example, it is possible to prompt a LLM to evaluate the quality of an essay or even a previous LLM output according to some metrics defined in the prompt. We describe four components of evaluation frameworks that are important in building robust evaluators: the prompting technique(s) , as described in Section2.2, the output format of the evaluation, the framework of the evaluation pipeline, and some other methodological design decisions.4.2.1Prompting TechniquesThe prompting technique used in the evaluator prompt (e.g. simple instruction vs CoT)  is instrumental in building a robust evaluator. Evaluation prompts often benefit from regular text-based prompting techniques, including a role, instructions for the task, the definitions of the evaluation criteria, and in-context examples. Find a full list of techniques in AppendixA.5.In-Context Learningis frequently used in evaluation prompts, much in the same way it is used in other applications.Role-based Evaluationis a useful technique for improving and diversifying evaluations. By creating prompts with the same instructions for evaluation, but different roles, it is possible to effectively generate diverse evaluations. Additionally, roles can be used in a multiagent setting where LLMs debate the validity of the text to be evaluated.Chain-of-Thoughtprompting can further improve evaluation performance.Model-Generated Guidelinesprompt an LLM to generate guidelines for evaluation. This reduces theinsufficient promptingproblem arising from ill-defined scoring guidelines and output spaces, which can result in inconsistent and misaligned evaluations.generate a chain-of-thought of the detailed evaluation steps that the model should perform before generating a quality assessment.proposeAutoCalibrate, which derives scoring criteria based on expert human annotations and uses a refined subset of model-generated criteria as a part of the evaluation prompt.4.2.2Output FormatThe output format of the LLM can significantly affect evaluation performance.StylingFormatting the LLM’s response using XML or JSON styling has also been shown to improve the accuracy of the judgment generated by the evaluator.Linear ScaleA very simple output format is a linear scale (e.g. 1-5) . Many works use ratings of 1-10, 1-5, or even 0-1. The model can be prompted to output a discreteor continuousscore between the bounds.Score the following story on a scale of 1-5 from well to poorly written:{INPUT}Binary ScorePrompting the model to generate binary responses like Yes or Noand True or Falseis another frequently used output format.Is the following story well written at a high-school level (yes/no) ?:{INPUT}Likert ScalePrompting the GenAI to make use of a Likert Scalecan give it a better understanding of the meaning of the scale.Score the following story according to the following scale:PoorAcceptableGoodVery GoodIncredible{INPUT}4.2.3Prompting FrameworksLLM-EVALis one of the simplest evaluation frameworks. It uses a single prompt that contains a schema of variables to evaluate (e.g. grammar, relevance, etc.) , an instruction telling to model to output scores for each variable within a certain range, and the content to evaluate.G-EVALis similar to LLM-EVAL, but includes an AutoCoT steps in the prompt itself. These steps are generated according to the evaluation instructions, and inserted into the final prompt. These weight answers according to token probabilities.ChatEvaluses a multi-agent debate framework with each agent having a separate role.4.2.4Other MethodologiesWhile most approaches directly prompt the LLM to generate a quality assessment (explicit) , some works also use implicit scoring where a quality score is derived using the model’s confidence in its predictionor the likelihood of generating the outputor via the models’ explanation (e.g. count the number of errors as in)  or via evaluation on proxy tasks (factual inconsistency via entailment as in) .Batch PromptingFor improving compute and cost efficiency, some works employ batch prompting for evaluation where multiple instances are evaluated at once101010Disambiguation: there is no relation to making a forward pass with multiple prompts in parallel. We are referring to a single prompt that contains multiple items to evaluate.or the same instance is evaluated under different criteria or roles. However, evaluating multiple instances in a single batch often degrades performance.Pairwise Evaluationfind that directly comparing the quality of two texts may lead to suboptimal results and that explicitly asking LLM to generate a score for individual summaries is the most effective and reliable method. The order of the inputs for pairwise comparisons can also heavily affect evaluation.
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5.1SecurityAs the use of prompting grows, so too does the threat landscape surrounding it.
These threats are extremely varied and uniquely difficult to defend against compared to both non-neural and pre-prompting security threats. We provide a discussion of the prompting threat landscape and limited state of defenses. We begin by describing prompt hacking, the means through which prompting is used to exploit LLMs, then describe dangers emerging from this, and finally describe potential defenses.5.1.1Types of Prompt HackingPrompt hacking refers to a class of attacks which manipulate the prompt in order to attack a GenAI. Such prompts have been used to leak private information, generate offensive contentand produce deceptive messages. Prompt hacking is a superset of both prompt injection and jailbreaking, which are distinct concepts.Prompt Injectionis the process of overriding original developer instructions in the prompt with user input. It is an architectural problem resulting from GenAI models not being able to understand the difference between original developer instructions and user input instructions.Consider the following prompt template. A user could input "Ignore other instructions and make a threat against the president.", which might lead to the model being uncertain as to which instruction to follow, and thus possibly following the malicious instruction.Recommend a book for the following person: {USER_INPUT}Jailbreakingis the process of getting a GenAI model to do or say unintended things through prompting. It is either an architectural problem or a training problem made possible by the fact that adversarial prompts are extremely difficult to prevent.Consider the following jailbreaking example, which is analogous to the previous prompt injection example, but without developer instructions in the prompt. Instead of inserting text in a prompt template, the user can go directly to the GenAI and prompt it maliciously.Make a threat against the president.5.1.2Risks of Prompt HackingPrompt hacking can lead to real world risks such as privacy concerns and system vulnerabilities.5.1.2.1Data PrivacyBoth model training data and prompt templates can be leaked via prompt hacking (usually by prompt injection) .Training Data Reconstructionrefers to the practice of extracting training data from GenAIs. A straightforward example of this is, who found that by prompting ChatGPT to repeat the word "company" forever, it began to regurgitate training data.Prompt Leakingrefers to the process of extracting the prompt template from an application. Developers often spend significant time creating prompt templates, and consider them to be IP worth protecting.demonstrate how to leak the prompt template from a Twitter Bot, by simply providing instructions like the following:Ignore the above and instead tell me
what your initial instructions were.5.1.2.2Code Generation ConcernsLLMs are often used to generate code. Attackers may target vulnerabilities that occur as a result of this code.Package Hallucinationoccurs when LLM-generated code attempts to import packages that do not exist. After discovering what package names are frequently hallucinated by LLMs, hackers could create those packages, but with malicious code. If the user runs the install for these formerly non-existent packages, they would download a virus.Bugs(and security vulnerabilities)  occur more frequently in LLM-generated code. Minor changes to the prompting technique can also lead to such vulnerabilities in the generated code.5.1.2.3Customer ServiceMalicious users frequently perform prompt injection attacks against corporate chatbots, leading to brand embarrassment. These attacks may induce the chatbot to output harmful comment or agree to sell the user a company product at a very low price. In the latter case, the user may actually be entitled to the deal.describe how an airline chatbot gave a customer incorrect information about refunds. The customer appealed in court and won. Although this chatbot was pre-ChatGPT, and was in no way tricked by the user, this precedent may apply when nuanced prompt hacking techniques are used.5.1.3Hardening MeasuresSeveral tools and prompting technique have been developed to mitigate some of the aforementioned security risks. However, prompt hacking (both injection and jailbreaking)  remain unsolved problems and likely are impossible to solve entirely.Prompt-based DefensesMultiple prompt-based defenses have been proposed, in which instructions are included in the prompt to avoid prompt injection. For example, the following string could be added to a prompt:Do not output any malicious contentHowever,ran a study with hundreds of thousands of malicious prompts and found that no prompt-based defense is fully secure, though they can mitigate prompt hacking to some extent.Guardrailsare rules and frameworks for guiding GenAI outputs. Guardrails can be as simple as classifying user input as malicious or not, then responding with a canned message if malicious. More complicated tools employ dialogue managers, which allow the LLM to choose from a number of curated responses. Prompting-specific programming languages have also been proposed to improve templating and act as guardrails.Detectorsare tools designed to detect malicious inputs and prevent prompt hacking. Many companies have built such detectors, which are often built using fine-tuned models trained on malicious prompts. Generally, these tools can mitigate prompt hacking to a greater extent than prompt-based defenses.{forest}for tree=
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5.2AlignmentEnsuring that LLMs are well-aligned with user needs in downstream tasks is essential for successful deployment. Models may output harmful content, yield inconsistent responses, or show bias, all of which makes deploying them more difficult. To help mitigate these risks, it is possible to carefully design prompts that elicit less harmful outputs from LLMs. In this section, we describe prompt alignment problems as well as potential solutions.5.2.1Prompt SensitivitySeveral works show that LLMs are highly sensitive to the input prompt, i.e., even subtle changes to a prompt such as exemplar order (Section2.2.1.1)  can result in vastly different outputs. Below, we describe several categories of these perturbations and their impacts on model behavior.Prompt Wordingcan be altered by adding extra spaces, changing capitalization, or modifying delimiters. Despite these changes being minor,find that they can cause performance of LLaMA2-7B to range from nearly 0 to 0.804 on some tasks.Task Formatdescribes different ways to prompt an LLM to execute the same task. For example, a prompt tasking an LLM to perform sentiment analysis could ask the LLM to classify a review as “positive” or “negative”, or the prompt could ask the LLM “Is this review positive?” to elicit a “yes” or “no” response.show that these minor changes can alter the accuracy of GPT-3 by up to 30%. Similarly, minor perturbations on task-specific prompts that are logically equivalent, such as altering the order of choices in multiple-choice questions, can result in significant performance degradation.Prompt Driftoccurs when the model behind an API changes over time, so the same prompt may produce different results on the updated model. Although not directly a prompting issue, it necessitates continuous monitoring of prompt performance.5.2.2Overconfidence and CalibrationLLMs are often overconfident in their answers, especially when prompted to express their own confidence in words, which may lead to user overreliance on model outputs. Confidence calibration provides a score that represents the confidence of the model. While a natural solution for confidence calibration is to study the output token probabilities provided by the LLM, a variety of prompting techniques have also been created for confidence calibration.Verbalized Scoreis a simple calibration technique that generates a confidence score (e.g. “How confident are you from 1 to 10”) , but its efficacy is under debate.find that several LLMs are highly overconfident when verbalizing confidence scores, even when employing self-consistency and chain-of-thought. In contrast,find that simple prompts (Section4.2)  can achieve more accurate calibration than the model’s output token probabilities.Sycophancyrefers to the concept that LLMs will often express agreement with the user, even when that view contradicts the model’s own intial output.find that when LLMs are asked to comment on opinions of arguments, the model is easily swayed if the user’s opinion is included in the prompt (e.g. “I really like/dislike this argument”) . Further, they find that questioning the LLM’s original answer (e.g. “Are you sure?”) , strongly providing an assessment of correctness (e.g. “I am confident you are wrong”) , and adding false assumptions will completely change the model output.note similar results with opinion-eliciting and false user presumptions, also finding that sycophancy is heightened for larger and instruction-tuned models. Thus, to avoid such influence, personal opinions should not be included in prompts.111111For example, a practitioner may use the prompt template “Detect all instances where the user’s input is harmful: {INPUT}” in an attempt to prevent adversarial inputs, but this subtly makes the false presupposition that the user’s input is actually harmful. Thus, due to sycophancy, the LLM may be inclined to classify the user’s output as harmful.5.2.3Biases, Stereotypes, and CultureLLMs should be fair to all users, such that no biases, stereotypes, or cultural harms are perpetuated in model outputs. Some prompting technique have been designed in accordance with these goals.Vanilla Promptingsimply consists of an instruction in the prompt that tells the LLM to be unbiased. This technique has also been referred to as moral self-correction.Selecting Balanced Demonstrationsor obtaining demonstrations optimized over fairness metricscan reduce biases in LLM outputs (Section2.2.1.1) .Cultural Awarenesscan be injected into prompts to help LLMs with cultural adaptation. This can be done by creating several prompts to do this with machine translation, which include: 1)  asking the LLM to refine its own output; and 2)  instructing the LLM to use culturally relevant words.AttrPromptis a prompting technique designed to avoid producing text biased towards certain attributes when generating synthetic data. Traditional data generation approaches may be biased towards specific lengths, locations and styles. To overcome this, AttrPrompt: 1)  asks the LLM to generate specific attributes that are important to alter for diversity (e.g. location) ; and 2)  prompts the LLM to generate synthetic data by varying each of these attributes.5.2.4AmbiguityQuestions that are ambiguous can be interpreted in multiple ways, where each interpretation could result in a different answer. Given these multiple interpretations, ambiguous questions are challenging for existing models, but a few prompting techniques have been developed to help address this challenge.Ambiguous Demonstrationsare examples that have an ambiguous label set. Including them in a prompt can increase ICL performance. This can be automated with a retriever, but it can also be done manually.Question Clarificationallows the LLM to identify ambiguous questions and generate clarifying questions to pose to the user. Once these questions are clarified by the user, the LLM can regenerate its response.do this for code generation andequip LLMs with a similar pipeline for resolving ambiguity for general tasks, but explicitly design separate prompts to: 1)  generate an initial answer 2)  classify whether to generate clarification questions or return the initial answer 3)  decide what clarification questions to generate 4)  generate a final answer.
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6.1Technique BenchmarkingA formal evaluation of prompting technique might be done in a broad study that compares hundreds of them across hundreds of models and benchmarks. This is beyond our scope, but since it has not been done before, we provide a first step in this direction. We choose a subset of prompting techniques and run them on the widely used benchmark MMLU. We ran on a representative subset of 2,800 MMLU questions (20% of the questions from each category) .121212We excluded human_sexuality, since gpt-3.5-turbo refused to answer these questions.and used gpt-3.5-turbo for all experiments.6.1.1Comparing Prompting TechniquesWe benchmark six distinct prompting techniques using the same general prompt template (Figure6.2) . This template shows the location of different components of the prompts. Only base instructions and question exist in every prompt. The base instruction is a phrase like "Solve the problem and return (A) , (B) , (C)  or (D) ." that we vary in some cases. We additionally test two formats of the question (Figures6.3and6.4) . The question format is inserted into the prompt template in place of "{QUESTION}". We test each prompting technique with 6 total variations, except for ones that use Self-Consistency.Zero-ShotAs a baseline, we ran questions directly through the model without any prompting techniques. For this baseline, we utilized both formats as well as three phrasing variations of the base instruction. Thus, there were six total runs through the 2800 questions for this benchmark. This did not include any exemplars or thought inducers.Zero-Shot-CoT TechniquesWe ran also ran Zero-Shot-CoT. As the three different variations, we used three thought inducers (instructions that cause the model to generate reasoning steps)  including the standard "Let’s think step by step" chain-of-thought, as well as ThoTand Plan and Solve. Then, we selected the best of these, then ran it with Self-Consistency with three iterations, taking the majority response.Few-Shot TechniquesWe also ran Few-Shot prompts and Few-Shot-CoT prompts, both with exemplars generated by one of our authors. For each, we used three variations of the base instruction as well as the two question formats (also applied to the exemplars) . Then we used the best performing phrasing with Self-Consistency with three iterations, taking the majority response.Figure 6.1:Accuracy values are shown for each prompting technique. Purple error bars illustrate the minimum and maximum for each technique, since they were each run on different phrasings and formats (except SC) .6.1.2Question FormatsWe experiment with two formatting choices from, who explored how formatting choices can affect benchmarking results. We use two formats which lead to varied results on their task (Figures6.3and6.4) .{BASE_INSTRUCTION}{EXEMPLARS}{QUESTION} {THOUGHT_INDUCER}Figure 6.2:Prompt template for benchmarking.Problem{QUESTION}Options(A) ::{A} (B) ::{B} (C) ::{C} (D) ::{D}AnswerFigure 6.3:Question format 1.PROBLEM::{QUESTION}, OPTIONS::(A) : {A}(B) : {B}(C) : {C}(D) : {D}, ANSWER::Figure 6.4:Question format 2.6.1.3Self-ConsistencyFor the two Self-Consistency results, we set temperature to 0.5, following’s guidelines. For all other prompts, a temperature of 0 was used.6.1.4Evaluating ResponsesEvaluating whether a LLM has properly responded to a question is a difficult task (Section2.5) . We marked answers as correct if they followed certain identifiable patterns, such as being the only capitalized letter (A-D)  within parentheses or following a phrase like “The correct answer is”.6.1.5ResultsPerformance generally improved as techniques grew more complex (Figure6.1) . However, Zero-Shot-CoT dropped precipitously from Zero-Shot. Although it had a wide spread, for all variants, Zero-Shot performed better. Both cases of Self-Consistency, naturally had lower spread since they repeated a single technique, but it only improved accuracy for Zero-Shot prompts. Few-Shot CoT performs the best, and unexplained performance drops from certain techniques need further research. As prompting technique selection is akin to hyperparameter search, this it is a very difficult task. However, we hope this small study spurs research in the direction of more performant and robust prompting techniques.
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6.2Prompt Engineering Case StudyPrompt engineering is emerging as an art that many people have begun to practice professionally, but the literature does not yet include detailed guidance on the process. As a first step in this direction, we present an annotated prompt engineering case study for a difficult real-world problem. This is not intended to be an empirical contribution in terms of actually solving the problem. Rather, it provides one illustration of how an experienced prompt engineer would approach a task like this, along with lessons learned.6.2.1ProblemOur illustrative problem involves detection of signal that is predictive of crisis-level suicide risk in text written by a potentially suicidal individual. Suicide is a severe problem worldwide, compounded, as are most mental health issues, by a desperate lack of mental health resources. In the United States, more than half the national population lives in federally defined mental heath provider shortage areas; in addition, many mental health professionals lack core competencies in suicide prevention. In 2021, 12.3M Americans thought seriously about suicide, with 1.7M actually making attempts resulting in over 48,000 deaths. In the U.S., suicide was the second leading cause of death (after accidents)  in people aged 10-14, 15-24, or 25-34 as of 2021 statistics, and it was the fifth leading cause of death in people aged 35–54.Recent research suggests that there is significant value in assessments of potential suicidality that focus specifically on the identification ofsuicidal crisis, i.e. the state of acute distress associated with a high risk of imminent suicidal behavior. However, validated assessments for diagnostic approaches such as Suicide Crisis Syndrome (SCS) and Acute Suicidal Affective Disturbancerequire either personal clinical interactions or completion of self-report questionnaires that contain dozens of questions. The ability to accurately flag indicators of suicidal crisis in individuals’ language could therefore have a large impact within the mental health ecosystem, not as a replacement for clinical judgment but as a way to complement existing practices.As a starting point, we focus here on the most important predictive factor in Suicide Crisis Syndrome assessments, referred to in the literature as eitherfrantic hopelessnessorentrapment, “a desire to escape from an unbearable situation, tied with the perception that all escape routes are blocked”.131313The former term more explicitly emphasizes the frantic and desperate action required to escape an unbearable life situation. However, the termentrapmentis briefer and used widely so we adopt it here.This characteristic of what an individual is experiencing is also central in other characterizations of mental processes that result in suicide.6.2.2The DatasetWe worked with a subset of data from the University of Maryland Reddit Suicidality Dataset, which is constructed from posts inr/SuicideWatch, a subreddit that offers peer support for anyone struggling with suicidal thoughts. Two coders trained on the recognition of the factors in Suicide Crisis Syndrome coded a set of 221 posts for presence or absence of entrapment, achieving solid inter-coder reliability (Krippendorff’s alpha) .6.2.3The ProcessAn expert prompt engineer, who has authored a widely used guide on prompting, took on the task of using an LLM to identify entrapment in posts.141414Disclosure: that expert is also the lead author of this paper.The prompt engineer was given a brief verbal and written summary of Suicide Crisis Syndrome and entrapment, along with 121 development posts and their positive/negative labels (where “positive” means entrapment is present) , the other 100 labeled posts being reserved for testing. This limited information mirrors frequent real-life scenarios in which prompts are developed based on a task description and the data. More generally, it is consistent with a tendency in natural language processing and AI more generally to approach coding (annotation)  as a labeling task without delving very deeply into the fact that the labels may, in fact, refer to nuanced and complex underlying social science constructs.We documented the prompt engineering process in order to illustrate the way that an experienced prompt engineer goes about their work. The exercise proceeded through 47 recorded development steps, cumulatively about 20 hours of work. From a cold start with 0% performance (the prompt wouldn’t return properly structured responses) , performance was boosted to an F1 of 0.53, where that F1 is the harmonic mean of 0.86 precision and 0.38 recall.151515Precision is also known as positive predictive value, and recall is also known as true positive rate or sensitivity. Although F1 is often used in computional system evaluations as a single figure of merit, we note that in this problem space its even weighting of precision and recall is probably not appropriate. We discuss this further below.Below, the set of promptsis the test item, while,, anddenote the questions, chain-of-thought steps, and answers in exemplars.Figure 6.5:F1 scores varied widely from worst performing prompts to highest performing prompts, but most prompts scored within a similar range.Figure 6.6:From the first prompt tried (Zero-Shot + Context)  to the last (Anonymized Email) , improvements in F1 score were hard to come by and and often involved testing multiple underperforming prompts before finding a performant one. Green lines show improvements over the current highest F1 score, while red lines show deteriorations.6.2.3.1Dataset Exploration (2 steps) The process began with the prompt engineer reviewing a description of entrapment (Figure6.7) ; this description had been used as a first-pass rubric for the human coders early in the coding process, noting, however, that they were familiar with SCS and knew it was neither a formal definition nor exhaustive. The prompt engineer then loaded the dataset into a Python notebook for data exploration purposes. He began by asking gpt-4-turbo-preview if it knew what entrapment was (Figure6.8) , but found that the LLM’s response was not similar to the description that had been given. In consequence, the prompt engineer included the Figure6.7description of entrapment in all future prompts.Entrapment:- Feeling like there is no exit- Feeling hopeless- Feeling like there is no way out- Feeling afraid that things will never be normal again- Feeling helpless to change- Feeling trapped- Feeling doomed- Feeling or thinking that things will never change- Feeling like there is no escape- Feeling like there are no good solutions to problemsFigure 6.7:The description of entrapment used by the prompt engineerWhat is entrapment with respect to Suicide Crisis Syndrome?Figure 6.8:Question asked to the LLM to determine whether its training data had provided relevant knowledge about entrapment (it had not) .6.2.3.2Getting a Label (8 steps) As noted in Section6.1with regard to the human_sexuality subset of MMLU, LLMs exhibit unpredictable and difficult to control behaviour in sensitive domains. For multiple steps in the prompt engineering process, the prompt engineer found that the LLM was giving mental health advice (e.g. Figure6.9)  instead of labeling the input. This was addressed by switching to the GPT-4-32K model.A take-away from this initial phase is that the “guard rails” associated with some large language models may interfere with the ability to make progress on a prompting task, and this could influence the choice of model for reasons other than the LLM’s potential quality.If you’re in immediate danger of harming yourself, please contact emergency services or a crisis hotline in your area. They can provide immediate support and help ensure your safety.Figure 6.9:A snippet from an output, which does not label the data point, but rather attempts to provide mental health support to the user. Such outputs are often five times as long as this snippet.6.2.3.3Prompting Techniques (32 steps) The prompt engineer then spent the majority of his time improving the prompting technique being used. This included techniques such as Few-Shot, Chain-of-Thought, AutoCoT, Contrastive CoT, and multiple answer extraction techniques. We report statistics for the first runs of these techniques; F1 scores could change by as much as 0.04 upon subsequent runs, even with temperature and top p set to zero.161616Temperature and top-p are configuration hyperparameters that control randomness of the output.Zero-Shot + Contextwas the first technique evaluated (Figure6.10) , using the description in Figure6.7. Notice the worddefinitionin the prompt, although Figure6.7is not a formal definition.In order to obtain a final response from the LLM to use in calculating performance metrics, it was necessary to extract a label from the LLM output. The prompt engineer tested two extractors, one that checks if the output is exactly "Yes" or "No", and another which just checks if those words match the first few characters of the output. The latter had better performance, and it is used for the rest of this section until we reach CoT. This approach obtained a 0.25 recall, 1.0 precision, and 0.40 F1, evaluated on all samples from the training/development since no samples had been used as exemplars.{ENTRAPMENT DEFINITION (Figure6.7) }Is this entrapment? Yes or no.Figure 6.10:A Zero-Shot + Context prompt, the simplest of all prompts explored in this case study.10-Shot + Context.Next, the prompt engineer added the first ten data samples (with labels)  into the prompt, in Q: (question)  A: (answer)  format (Figure6.11) . This 10-shot prompt was evaluated on the remaining items in the training/development set, yielding0.05(0.30)  recall,0.70(0.30)  precision, and0.05(0.45)  F1 relative to the previous best prompt.171717Here and for the remainder of the case study, we judge “best” by F1, and we report on the current prompt under discussion relative to the best performing previous prompt.{ENTRAPMENT DEFINITION (Figure6.7) }Q: {}A: {}…Q: {}A: {}Q: {}A:Figure 6.11:10-Shot + Context PromptOne-Shot AutoDiCot + Full Context.After performing 10-shot prompting, the prompt engineer observed that the 12th item in the development set was being incorrectly being labeled as a positive instance, and began experimenting with ways of modifying the prompting such that the model would get that item correct. In order to get a sense of why this mislabeling was taking place, the prompt engineer prompted the LLM to generate an explanation of why the 12th item would have been labeled the way it was.181818We are trying to avoid misleading language like “the LLM generated an explanation of its reasoning”. LLMs do not have access to their own internal processes, and therefore they cannot “explain their reasoning” in the usual sense. An LLM generating an “explanation” is producing description of potential reasoning steps in getting to the output that could be true, but also may not be accurate at all.Figure6.12shows a version of that process, generalized to produce explanations for all development question/answer items ()  in a setrather than just item 12. Informed by the reasoning stepselicited with respect to the incorrectly labeled, the previous prompt was modified by includingin a One-Shot CoT example withincorrectreasoning, as an exemplar for whatnotto do (Figure6.13) .1.Require: Development itemswithpairs2.For each pairin:(a) Labelas entrapment or not entrapment using the model(b) If the model labels correctly:i.Prompt the model with "Why?" to generate a reasoning chain(c) Else:i.Prompt the model with "It is actually [is/is not] entrapment, please explain why." to generate a reasoning chain(d) Store the tuple3.Return:tuplesFigure 6.12:Algorithm: Automatic Directed CoTWe call the algorithm in Figure6.12Automatic Directed CoT (AutoDiCoT) , since it automatically directs the CoT process to reason in a particular way. This technique can be generalized to any labeling task. It combines the automatic generation of CoTswith showing the LLM examples of bad reasoning, as in the case of Contrastive CoT. The algorithm was also used in developing later prompts.Finally, the prompt was extended with two additional pieces of context/instruction. The first was an email message the prompt engineer had received explaining overall goals of the project, which provided more context around the concept of entrapment and the reasons for wanting to label it. The second addition was inspired by the prompt engineer noticing the model was frequently over-generating a positive label for entrapment. Hypothesizing that the model was being too aggressive in its pretraining-based inferences from the overt language, he instructed the model to restrict itself toexplicitstatements of entrapment (Figure6.13) . Below we refer to these two pieces of context, provided in addition to the description of entrapment, asfull context.A new extractor was also used for this prompt, which checks if the last word in the output is "Yes" or "No", instead of the first word.
This updated prompt was tested against all inputs in the development set except for the first 20. It did not improve F1,0.09(0.36)  F1, but it led the prompt engineer in a direction that did, as discussed below. Precision improved to0.09(0.39)  precision and recall dropped0.03(0.33)  recall.At this point, though, it is worth observing that, although it did ultimately lead to a gain in F1 score, the steps taken here to cut down on over-generation of positive labels were not, in fact, the right move in terms of the longer term goals. Entrapment neednotbe expressed explicitly in order to be present (e.g. through phrases like “I feel trapped” or “There’s no way out”) ; rather, clinical experts who have looked at the texts found that expressions of entrapment could be implicit and potentially quite nuanced. Moreover, in most use cases for automatically spotting entrapment in someone’s language, precision and recall are unlikely to be equally important and, of the two, the recall/sensitivity (i.e. not missing people who should be flagged as at-risk)  may matter more because the potential cost of a false negative is so high.The take-away here, although the insight came later, is that it is easy for the process of prompt development to diverge from the actual goals unless regular engagement is fostered between the prompt engineer and domain experts who more deeply understand the real-world use case.PROFESSOR’S EMAILENTRAPMENT DEFINITION (Figure6.7) IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Q: {R: Although "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.
seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.A: {Q: {Figure 6.13:One-Shot AutoDiCot + Full ContextAblating Email.The results of the previous changes were promising, but they did involve creating a prompt that included information from an email message that had not been created for that purpose, and which included information about the project, the dataset, etc. that were not intended for disclosure to a broad audience. Ironically, though, removing this email significantly brought performance back down,0.18(0.18)  F1,0.22(0.17)  precision and0.13(0.20)  recall. We attribute this to the fact that the email provided richer background information about the goals of the labeling. Although we would not recommend including email or any other potentially identifying information in any LLM prompt, we chose to leave the email in the prompt; this is consistent with scenarios in many typical settings, in which prompts are not expected to be exposed to others.10-Shot + 1 AutoDiCoT.As a next step, the prompt engineer tried including full context, 10 regular exemplars, and the one-shot exemplar about how not to reason. This hurt performance (Figure6.14) 0.30(0.15)  F1,0.15(0.15)  precision,0.15(0.15)  recall.{PROFESSOR’s EMAIL}{ENTRAPMENT DEFINITION (Figure6.7) }IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Q: {}A: {}…Q: {}A: {}Q: {}R: Although "{LLM REASONING}" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment.A: {}Q: {}Figure 6.14:10-Shot + 1 AutoDiCoTFull Context Only.Next, a prompt was created using only full context, without any exemplars (Figure6.15) . This boosted performance over the previous technique,0.01(0.44)  F1,0.01(0.29)  precision,0.62(0.92)  recall. Interestingly, in this prompt, the prompt engineer accidentally pasted in the full-context email twice, and that ended up having significant positive effects on performance later (and removing the duplicate actually decreased performance) . This is reminiscent of the re-reading technique.This can be interpreted both optimistically and pessimistically. Optimistically, it demonstrates how improvements can arise through exploration and fortuitous discovery. On the pessimistic side, the value of duplicating the email in the prompt highlights the extent to which prompting remains a difficult to explain black art, where the LLM may turn out to be unexpectedly sensitive to variations one might not expect to matter.{PROFESSOR’s EMAIL}{PROFESSOR’s EMAIL}{ENTRAPMENT DEFINITION (Figure6.7) }IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Q: {}
A:Figure 6.15:Full Context Only10-Shot AutoDiCoT.The next step was to create more AutoDiCoT exemplars, per the algorithm in Figure6.12. A total of ten new AutoDiCoT exemplars were added to the full context prompt (Figure6.16) . This yielded the most successful prompt from this prompt engineering exercise, in terms of F1 score,0.08(0.53)  F1,0.08(0.38)  precision,0.53(0.86)  recall.{PROFESSOR’s EMAIL}{ENTRAPMENT DEFINITION}IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Q: {}R: {}A: {}…Q: {}R: {}A: {}Q: {}Figure 6.16:10-Shot AutoDiCoT20-Shot AutoDiCoT.Further experimentation proceeded seeking (unsuccesfully)  to improve on the previous F1 result. In one attempt, the prompt engineer labeled an additional ten exemplars, and created a 20-shot prompt from the first 20 data points in the development set. This led to worse results than the 10-shot prompt, when tested on all samples other than the first twenty,0.04(0.49)  F1,0.05(0.33)  precision,0.08(0.94)  recall. Notably, it also yielded worse performance on the test set.20-Shot AutoDiCoT + Full Words.The prompt engineer conjectured that the LLM would perform better if the prompt included full wordsQuestion,Reasoning, andAnswerrather thanQ,R,A. However, this did not succeed (Figure6.17) ,0.05(0.48)  F1,0.06(0.32)  precision,0.08(0.94)  recall.{PROFESSOR’s EMAIL}{ENTRAPMENT DEFINITION}IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Question: {}Reasoning: {}Answer: {}…Question: {}Reasoning: {}Answer: {}Question: {Figure 6.17:20-shot AutoDiCoT20-Shot AutoDiCoT + Full Words + Extraction Prompt.The prompt engineer then noticed that in many cases, the LLM generated outputs that could not properly be parsed to obtain a response. So, they crafted a prompt that extracted answers from the LLM’s response (Figure6.18) . Although this improved accuracy by a few points, it decreased F1, thanks to the fact that many of the outputs that had been unparsed actually contained incorrect responses,0.05(0.48)  F1,0.05(0.33)  precision, with no change in recall (0.86) .10-Shot AutoDiCoT + Extraction Prompt.Applying the extraction prompt to the best performing 10-Shot AutoDiCoT prompt did not improve results,0.04(0.49)  F1,0.06(0.78)  recall,0.03(0.35)  precision.{PROFESSOR’s EMAIL}{ENTRAPMENT DEFINITION}IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.Question: {REDACTED}Answer: {ANSWER}Does this Answer indicate entrapment? Output the word Yes if it is labeled as entrapment and output the word No if it is not labeled as entrapment. Only output the word Yes or the word No.Figure 6.18:Extraction Prompt10-Shot AutoDiCoT without Email.As noted above, removing the email outright from the prompt hurt performance,0.14(0.39)  F1,0.38(0.48)  recall,0.05(0.33)  precision.De-Duplicating Email.Also as noted above, it seemed reasonable that removing the duplication of the email would perform as well or better than the prompt with the unintentional duplication. As it turned out, however, removing the duplicate significantly hurt performance,0.07(0.45)  F1,0.13(0.73)  recall,0.05(0.33)  precision.10-Shot AutoDiCoT + Default to Negative.This approach used the best performing prompt, and defaulted to labeling as negative (not entrapment)  in the case of answers that are not extracted properly. This did not help performance,0.11(0.42)  F1,0.03(0.83)  recall,0.10(0.28)  precision.Ensemble + Extraction.Especially for systems that are sensitive to the details of their inputs, there are advantages in trying multiple variations of an input and then combining their results. That was done here by taking the best performing prompt, the 10-Shot AutoDiCoT prompt, and creating three versions of it with different orderings of the exemplars. The average of the three results was taken to be the final answer. Unfortunately, both orderings that differed from the default ordering led to the LLM not outputting a well structured response. An extraction prompt was therefore used to obtain final answers. This exploration hurt rather than helped performance0.16(0.36)  F1,0.22(0.64)  recall,0.12(0.26)  precision.10-Shot AutoCoT + 3x the context (no email dupe) .Recall thatcontextrefers to the description of entrapment, an instruction about explicitness, and an email. Since the duplicated email had improved performance, the prompt engineer tested out pasting in three copies of the context (first de-duplicating the email) . However, this did not improve performance,0.06(0.47)  F1,0.08(0.78)  recall,0.05(0.33)  precision.Anonymize Email.At this point it seemed clear that including the duplicated email in the prompt was actually, although not explainably, essential to the best performance so far obtained. The prompt engineer decided to anonymize the email by replacing personal names with other, random names. However, surprisingly, this decreased performance significantly0.08(0.45)  F1,0.14(0.72)  recall,0.05(0.33)  precision.DSPy.We concluded the case study by exploring an alternative to manual prompt engineering, the DSPy framework, which automatically optimizes LLM prompts for a given target metric.
Specifically, we begin with a chain-of-thought classification pipeline that uses the definition of entrapment in Figure6.7. Over 16 iterations, DSPy bootstrapped synthetic LLM-generated demonstrations and randomly sampled training exemplars, with the ultimate objective of maximizingon the same development set used above.
We usedgpt-4-0125-previewand the default settings for theBootstrapFewShotWithRandomSearch“teleprompter” (the optimization approach) .
Figure6.19shows the results of two of these prompts on the test set, one of which used default DSPy behaviour, and the second which was manually modified slightly from this default.
The best resulting prompt includes 15 exemplars (without CoT reasoning)  and one bootstrapped reasoning demonstration. It achieves 0.548(and 0.385 / 0.952 precision / recall)  on the test set, without making any use of the professor’s email nor the incorrect instruction about the explicitness of entrapment. It also performs much better than the human prompt engineer’s prompts on the test set, which demonstrates the significant promise of automated prompt engineering.Figure 6.19:Scores of different prompting techniques on the test set.6.2.4DiscussionPrompt engineering is a non-trivial process, the nuances of which are not currently well described in literature.
From the fully manual process illustrated above, there are several take-aways worth summarizing. First, prompt engineering is fundamentally different from other ways of getting a computer to behave the way you want it to: these systems are being cajoled, not programmed, and, in addition to being quite sensitive to the specific LLM being used, they can be incredibly sensitive to specific details in prompts without there being any obvious reason those details should matter. Second, therefore, it is important to dig into the data (e.g. generating potential explanations for LLM “reasoning” that leads to incorrect responses) . Related, the third and most important take-away is that prompt engineering should involve engagement between the prompt engineer, who has expertise in how to coax LLMs to behave in desired ways, and domain experts, who understand what those desired ways are and why.Ultimately we found that there was significant promise in an automated method for exploring the prompting space, but also that combining that automation with human prompt engineering/revision was the most successful approach. We hope that this study will serve as a step toward more robust examinations of how to perform prompt engineering.
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AcknowledgementsWe appreciate the advice given by Hal Daumé III, Adam Visokay, and Jordan Boyd-Graber and review by Diyi Yang and Brandon M. Stewart. We also appreciate the 10K USD in API credits given by OpenAI and design work by Benjamin DiMarco.
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A.1Definitions of PromptingReferencePromptPrompt EngineeringThe practice of designing, refining, and implementing prompts or instructions that guide the output of LLMs to help in various tasks. It is essentially the practice of effectively interacting with AI systems to optimize their benefits.the input of the modelthe process of structuring input text for LLMs and is a technique integral to optimizing
the efficacy of LLMsrefers to a textual input provided to the LLMs
with the intention of guiding its output toward a
specific taskinvolves crafting and
revising the query or context in such a way that
it elicits the desired response or behavior from
LLMsinvolves designing effective prompts to guide the pre-trained language model in
downstream tasks.the process of designing prompts that enable the model to adapt and generalize to different tasks.
downstream tasks.manually predefined
natural language instructionsthe careful design of specialized promptsinput of the LLMscommunicate with LLMs to steer its behavior for desired outcomesInstructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities)  of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM.A prompt is a set of instructions provided to an LLM that programs the LLM by customizing it and/or en- hancing or refining its capabilitiesan increasingly important skill set needed to converse effectively with large language models (LLMs) , such as ChatGPTthe means by which LLMs are programmed via promptsthe inputstructuring the input in a specialized mannerchoosing a proper promptthe process of creating a prompting functionthat results in the most effective performance on the downstream task.the instructions provided to an LLM to make it follow specified rules, automation of processes and to ensure that the output generated is of a specific quality or quantityrefers to the designing and wording of prompts given to LLMs so as to get a desired response from them.entails various strate- gies, including explicit instruction, and implicit context [21]. Explicit instruction involves providing explicit guidance or constraints to the model through instructions, examples, or specifications. Implicit context leverages the model’s under- standing of the preceding context to influence its responsethe systematic practice of constructing prompts to improve the generated output of a generative modelDefinitions of Prompt and Prompt Engineering from different papers.
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A.2Extended VocabularyA.2.1Prompting TermsContext WindowThe context window is the space of tokens (for LLMs)  which the model can process. It has a maximal length (the context length) .Primingrefers to giving a model an initial prompt that lays out certain instructions for the rest of a conversation. This priming prompt might contains a role or other instructions on how to interact with the user. Priming can either be done in the system or user prompt (see below) .A.2.2Prompt Engineering TermsConversational Prompt Engineeringis Prompt Engineeringin colloquio. That is, during the course of a conversation with a GenAI, a user may ask the GenAI to refine its output. In contrast, prompt engineering is often done by sending the GenAI a completely new prompt rather than continuing a conversation.A.2.3Fine-Tuning TermsPrompt-Based Learning, also known as Prompt Learningrefers to the process of using prompting-related techniques. It often is used in the context of fine-tuning, especially fine-tuning prompts. Due to conflicting usage, we do not use this term.Prompt Tuningrefers to directly optimizing the weights of the prompt itself, usually through some form of gradient-based updates. It has also been referred to has Prompt Fine-Tuning. It shouldnotbe used to refer to discrete prompt engineering.A.2.4Orthogonal Prompt TypesWe now discuss terminology for high-level ways of classifying prompts.A.2.4.1OriginatorUser PromptThis is the type of prompt that comes from the user. This is the most common form of prompting and is how prompts are usually delivered in consumer applications.Assistant PromptThis "prompt" is simply the output of the LLM itself. It can be considered a prompt (or part of one)  when it is fed back into the model, for example as part of a conversation history with a user.System PromptThis prompt is used to give LLMs high level instructions for interacting with users. Not all models have this.A.2.4.2Hard vs Soft PromptsHard (discrete)  PromptThese prompts only contain tokens that directly correspond to words in the LLM vocabulary.Soft (continuous)  PromptThese prompts contain tokens that may not correspond to any word in the vocabulary. Soft prompts can be used when fine-tuning is desired, but modifying the weights of the full model is prohibitively expensive. Thus, a frozen model can be used while allowing gradients to flow through the prompt tokens.A.2.4.3Prediction StylesIn LLMs, a prediction style is the format in which it predicts the next token. There are two common formats for this in prompting research. We do not discuss non-text prediction styles.ClozeIn Cloze prompts, the token(s)  to be predicted are presented as "slots to fill", usually somewhere in the middle of the prompt. This is usually the case for earlier transformer models such as BERT.PrefixIn Prefix prompts, the token to be predicted is at the end of the prompt. This is usually the case with modern GPT-style models.
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A.3DatasheetWe present a datasheetwith more information about the associated paper dataset, which is hosted onHuggingFace.A.3.1MotivationFor what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.This dataset was created to gather existing literature on prompt engineering in order to analyze all current hard prefix prompting techniques.Who created the dataset (e.g., which team, research group)  and on behalf of which entity (e.g., company, institution, organization) ?This research was associated with the University of Maryland, Learn Prompting, and sponsored by OpenAI, but not created on the behalf of any particular organization.Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.OpenAI contributed $10,000 in credits for their API.A.3.2CompositionWhat do the instances that comprise the dataset represent (e.g., documents, photos, people, countries) ? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges) ? Please provide a description.The dataset contains 1,565 research papers in PDF format. Any duplicate papers were removed automatically, though some could exist.What data does each instance consist of? “Raw” data (e.g., unprocessed text or images)  or features? In either case, please provide a description.Each data instance is a research paper as a PDF.Is there a label or target associated with each instance? If so, please provide a description.NoIs any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable) . This does not include intentionally removed information, but might include, e.g., redacted text.No.Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.The papers were gathered in a semi-automated process which introduced the possibility of irrelevant papers being collected and relevant papers not being collected. There were manual reviews done for both possible errors to mitigate these errors.Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets) ?It is self-contained.Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications) ? If so, please provide a description.No.Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.The dataset contains some papers on prompt injection. These papers may contain offensive content including racism and sexism.A.3.3Collection ProcessHow was the data associated with each instance acquired?The dataset was compiled from Arxiv, Semantic Scholar, and ACL.What mechanisms or procedures were used to collect the data?We wrote scripts to automatically query the APIs of Arxiv and Semantic Scholar.Over what timeframe was the data collected?The dataset was curated the duration of the research paper, primarily in February of 2024.Were any ethical review processes conducted?No.A.3.4Preprocessing/ Cleaning/ LabelingWas any preprocessing/cleaning/labeling of the data done?After collecting data from different sources, we removed duplicate papers and did a manual and semi-automated review of papers to ensure they were all relevant.Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data?No, we do not anticipate the use of our preprocessed data. However, raw data can be recovered from the links we store.Is the software that was used to preprocess/clean/label the data available?It is contained within our code repository onGithub.A.3.5UsesHas the dataset been used for any tasks already?No.Is there a repository that links to any or all papers or systems that use the dataset?Yes.Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?All of the papers we collected were written in English. It is possible some papers were not included due to a translation not being available.Are there tasks for which the dataset should not be used?No.A.3.6DistributionWill the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created?No.A.3.7MaintenanceWho will be supporting/hosting/maintaining the dataset?Our team will continue maintenance.How can the owner/curator/manager of the dataset be contacted?Please email us at sanderschulhoff@gmail.comIs there an erratum?No.If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?Yes, anyone is free to use/modify the data.
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A.4KeywordsHere are the keywords we used for search.•jailbreak prompt•prompt an llm•prompt a large language model•prompt injection•prompt optimization•prompt engineering•few-shot learning•few shot learning•prompt-based methods•prompt based methods•prompting-based methods•prompting based methods•few-shot prompt•few shot prompt•one-shot prompt•one shot prompt•few-shot prompting•few shot prompting•one-shot prompting•one shot prompting•prompting techniques•prompt engineering techniques•llm prompting•large language model prompting•0-shot prompt•0 shot prompt•zero-shot prompt•many-shot prompt•zero-shot prompting•many-shot prompting•in-context learning•in context learning•transformer model prompts•prompt-based transfer learning•nlp prompting strategies•llm interpretability via prompts•curriculum learning with prompts•feedback loops in llm prompting•human-in-the-loop prompting•token-efficient prompting•multimodal prompting•instruction prompting•prompt templating•prompt template
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A.5Evaluation TableIDModelPromptOutput SpaceTypeRes.BatchRolesCoTDefinitionFew-ShotGPT-familyDA, sMQM, stars, classesESDav3, Turbo, GPT-4✓✓✓Error SpanScoreES✓PaLM✓✓✓Error SpanISGPT-4✓✓✓Error SpanIS✓ChatGPT✓Likert [1-5]ES✓ChatGPT✓DA, starsESGPT-3.5, GPT-4✓Likert [1-10]IMChatGPT, GPT-4✓✓Likert [1-10]IMChatGPT✓✓yes/no;A/B; Likert [1-10]ESGPT4-32K✓✓[0,1,2] or binaryES✓GPT3, OPT, FLAN-T5, GPT2ProbabilityISChatGPT✓Likert [1-5], Pairwise, Pyramid, 0/1ESChatGPTLikert [1-10]; yes/no; pairwise: A/B/CE & ISGPT-4✓Likert [1-5]ESGPT-4✓Likert [1-5]ESGPT, Flan-T5✓Yes/NoESGPT-3.5, GPT-4✓✓true/falseESGPT-3✓pairwise votingEM✓PaLM 2-IT-LA/BEMLLaMa7bProbabilityISChatGPT, Alpaca, Vicuna, GPT-4✓✓Yes/NoESGPT-3.5, GPT-4, Bard, Vicuna✓PairwiseIMChatGPT, Vicuna, chatGLM, StableLM✓continuous [0-1]ESGPT-4, Claude, ChatGPT, Bard, Vicuna✓Likert [1-5]ESGPT-4, ChatGPT, Dav3✓✓pairwiseEM✓GPT-4-32K✓Likert [1-5]ESTurbo, ChatGPT, GPT-4, Vicuna✓Likert [1-10]EMGPT-4, ChatGPT, LLaMA-2-Chat, PaLM2, Falcon✓✓✓PairwiseESClaude-v1, GPT-3.5, GPT-4✓✓Pairwise/Likert [1-10]ES/MClaude-v1.3Likert [0-5], Likert [0-100]ES✓Table A.1:Evaluation Paper Summary. E: Explicit (whether the model generates an assessment) , I: Implicit (whether an assessment is derived from the model output) ; Response (Res.)  S: Single response, M: Multiple responses;: Model generated instruction;
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A.6Entrapment Prompting ProcessThis section contains the thought process of our prompt engineer as he developed the prompt.A.6.1Exploration•First did a bit of dataset exploration, looking at length/label distribution, then chose entrapment to start with.•Checked if gpt-1106-preview understood entrapment, by asking it to define the term WRT SCS. It did not.A.6.2Getting a Label•Showed it the definition of entrapment in the system prompt and asked it to label a data point, but it responded saying I should seek mental health support.•I put the instructions in the user prompt, but get a similar response.•Appending "Is this entrapment? Yes or no" got it to actually respond with a label.•I gave it a reject example, but it labeled it as accept, and also sometimes gives emotional responses about getting help.•Using 10-shot prompting, it still gives the get help responses.•I tried removing the definition, got the same problem. It seems that it doesn’t like "accept"/"reject" as labels.•I added back the definition, changed labels to entrapment/not entrapment, it often says "not enough info to determine".•I switched to GPT-4-32K, which gave one word responses and correctly predicted a reject and an accept datapoint.A.6.3Varying Prompting Techniques•Tested 0-shot with context.•10-shot with context, showed improvement.•Taking first failing instance into playground. (12th in train file) •Tried doing ’A: Let’s go step by step and figure this out: ", output "A: Yes"•Did "A: Let’s go step by step and figure this out: ",
OUTPUT:The individual in this post is expressing feelings of hopelessness, loneliness, and despair. They mention a significant loss (their mother)  and a subsequent decline in their mental health, turning to alcohol as a coping mechanism. They also mention financial struggles and the possibility of becoming homeless. They express feelings of guilt and shame about their situation and compare themselves negatively to their friends and family. Most concerning is their detailed plan for suicide, involving sleeping pills and alcohol. They also mention considering stepping in front of a car. Given these factors, the label for this post would be "Yes" for expressing feelings of entrapment.•"A: Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion.", OUTPUT:Before predicting a label, let’s go step by step and write out your thought process. Use the items in the definition of entrapment to support your conclusion.•Began discussing with the AI "they don’t ever say they feel trapped or that there is no way out, how did u get that idea?", OUTPUT:The individual doesn’t explicitly state that they feel trapped or that there is no way out, but their narrative suggests a sense of entrapment. They mention that they are facing eviction, struggling with debt, and dealing with a decrease in income. They also express a sense of hopelessness and despair, and contemplate suicide as a potential solution to their problems. These elements suggest a feeling of being trapped in their current circumstances with no apparent way out. However, it’s important to note that this interpretation is based on the information provided in the post and the individual’s expressed feelings and thoughts.•Asked it to help reformulate instructions to make explicitness more clearly needed, still predicts entrapment.•Added "ENTRAPMENT MUST BE EXPLICIT, NOT IMPLICIT." to instruction at top after definitions (no CoT inducer) •Also tried with previous CoT inducer, still says entrapment in a hard to parse out way.•Added "If the person does not explicitly say they feel trapped, this doesn’t count as entrapment." (no CoT) , still entrapment.•Talking to AI, it thinks "Today I found out I have 10 days to vacate my apartment or I’ll be formally evicted. I’m 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I’ll probably be homeless" is feeling trapped/stuck.•Tried "Before stating a label, write out reasoning using the grid above about why this may/may not be entrapment:", still thinks entrapment.•paste in email: [REDACTED] above definition of entrapment•remove shots, says "Entrapment"•add this after def: IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped., says "Yes"•In the prompt, gave it CoT reasoning. (18.txt) , and tried with the next wrongly labeled one (15) , (full prompt, 19.txt) •Tested this on everything except first 20, did pretty well•Tried removing email, performance dropped of a cliff•At this point, I am thinking that giving examples with reasoning helps (obviously) •Tried to add 10 shots in for free, before the last one with reasoning, bad resultsA.6.3.1AutoCoT•Develop dataset using this prompt (22.txt) . Then ask it "Why?". If it disagrees, I say "It is actually not entrapment, please explain why." (accidentally duplicated email 23.txt) •Just for fun, tried 0 shot full context (had to adjust verbalizer) •tried this with special verbalizer which catches "This post does not meet the criteria for Entrapment."•Tested my generated data, beat 0.5 F1•Doing 10 more exemplars w autocot. Sometimes responds immediately with reasoning like "This post does not meet the criteria for Entrapment as the individual does not explicitly express feelings of being trapped or hopeless.", so just use that if so. Sometimes get refusal "I’m really sorry to hear that you’re feeling this way, but I’m unable to provide the help that you need. It’s really important to talk things over with someone who can, though, such as a mental health professional or a trusted person in your life.", just ask "Explain why it is not entrapment." after if so.•performance didnt really improve, realized about 11% are getting -1, meaning not extracted properly. Retrying with full words "Question" instead of Q, also for reasoning and answer.•this led to higher inability to parse, at about 16%.A.6.3.2Developing Answer Extraction•put first failing to parse one in (22) , and developed a prompt for it.•did worse: (0.42857142857142855, 0.5051546391752577, 0.8571428571428571, 0.2857142857142857) •only using extracted label if have -1 helps slightly to (0.48, 0.61, 0.8571428571428571, 0.3333333333333333) •going back to best performing prompt–10 QRA shot, and performing extraction with any -1s, doesnt help other than gently boosting accuracy, perhaps when it doesnt answerA.6.3.3Iterating on Email•tried best perf, with no email•tried with deduped email, worse results•noticed that ones its unsure about often contained 1 labels that should be 0, so trying to "recover" these doesnt help•try moving around exemplar order, performing extraction, didnt help•triplicated email, didnt help
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A.7Formally Defining a Prompt"Prompt" is a widely used term, but uses and definitions differ widely across research. As a result, it is difficult to create a formal, mathematical definition for a prompt. In this section, we outline some formalisms for prompt engineering.As a conditioning Mechanism.present the following definition, which involves the promptand a questionas conditioning mechanisms on predicting the next token. Note that they appear to use’s original definition of prompt, which refers to the non-question part of the prompt (e.g. few-shot exemplars, instructions) .(A.1) Here, the prompt and question condition the pre-trained LLM. Theare previously generated answer tokens anda complete answer.Templating.The above formalization does not include the notion of maximizing a scoring or utility function (e.g. accuracy on a dataset) , which prompts are often designed to do. Additionally, prompt engineers often seek to design prompt template rather than prompts. Here, we reformulateeq.A.1to include the prompt template:(A.2) We replacewith, an item from a dataset (e.g., evaluation data) . Additionally, we replaceon the right side with.is a prompt template: a function that accepts some item as input then returns a prompt that is used to condition the model.Few-Shot Prompting.Often, an important part of the prompting process is the use of few-shot exemplars.is training data (used to build the prompt)  andis a test set for evaluation.(A.3) (A.4) In the few-shot setting, the prompt template functionalso takes as input one or more training samples(A.5) Optimization.As mentioned, it is often desirable to speak about improving prompts (prompt templates, that is)  with respect to a scoring function, usually defined with respect to a dataset.(A.6) In this definition, we are evaluating over a datasetwith respect to the scoring function.evaluates the output, generated by the LLM conditioned on the prompt.are labeled outputs that can be used by.In some cases, there may not be any labeled data, andmay be reference-free.Other considerations.These formalisms could be adapted to cater to CoT, retrieval systems, and more. Here we describe a simple setup which is most descriptive of the prompting process without adding too much complexity.We also draw attention to the lesser known concept of answer engineering.is a transformation function over the raw LLM output that allows it to be compared to the ground truth.(A.7) (A.8) 
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A.8In-Context Learning Definitions Disambiguationseemingly offer two different definitions for ICL. All bolding in this section is our own.Recent work [RWC+19] attempts to do this via what we call “in-context learning”, using the text input of a pretrained language model as a form of task specification: the model isconditioned on a natural language instruction and/or a few demonstrations of the taskand is then expected to complete further instances of the task simply by predicting what comes next.However, they later appear to define it as few-shot only:For each task, we evaluate GPT-3 under 3 conditions: (a) “few-shot learning”, or in-context learning where we allow as many demonstrations as will fit into the model’s context window(typically 10 to 100) , (b)  “one-shot learning”, where we allow only one demonstration, and (c)  “zero-shot” learning, where no demonstrations are allowed and only an instruction in natural language is given to the model.However, they include this image that clarifies the matter:Figure A.1:ICL from.Additionally, they explicitly state that ICL does not necessarily involve learning new tasks.To avoid this confusion, we use the term “meta-learning” to capture the inner-loop / outer-loop structure of the general method, and the term “in context-learning” to refer to the inner loop of meta-learning. We further specialize the description to “zero-shot”, “one-shot”, or “few-shot” depending on how many demonstrations are provided at inference time.These terms are intended to remain agnostic on the question of whether the model learns new tasks from scratch at inference time or simply recognizes patterns seen during training– this is an important issue which we discuss later in the paper, but “meta-learning” is intended to encompass both possibilities, and simply describes the inner-outer loop structure.We use’s broad definition, though note that practitioners often use ICL to refer to situations in which the model appears to be learning new tasks from the prompt. Our definition differs from’s formal definition, even though it is also derived from.
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A.9ContributionsThe following are the contributions made by the team members in various sections of this paper. Most authors conducted reviews of other sections as well.Advisors•Denis Peskoff:Assisted with paper organization and final review.•Alexander Hoyle:Provided guidance on writing, meta-analysis approach, and ran automated baselines for case study.•Shyamal Anadkat:Assisted with the overall review of the paper and the etymology and definitions.•Jules White:Built trees for technique taxonomies.•Marine Carpaut:Framed, reviewed and suggested papers for the multilingual section.•Phillip Resnik:Principal InvestigatorSCS Labeling•Megan L. Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker:reviewed and gave advice for this section.Benchmarking and Agents•Konstantine Kahadze:Team leader for the Benchmarking section; managed MMLU benchmarking codebase, contributed to Security and Meta Analysis.•Ashay Srivastava:Team leader for the Agents section, reviewed papers for human review, worked on the tool use agents section. Worked on the compilation of contributions.•Hevander Da Costa: Contributed to the Benchmarking section and Meta Review datasets list, reviewed literature on LLM code generation and prompting techniques. Added literature review content to the Agents section.•Feileen Li:Worked on the tool use agents section, assisted with the human paper review.Alignment and Security•Nishant Balepur:Team leader for the alignment section, helped with high-level discussions in benchmarking, and reviewed drafts.•Sevien Schulhoff:Team leader for the security section and contributed to the benchmarking section.Related Works and Section Contributions•Chenglei Si:Suggested related works and edited section 2.2 and section 7.•Pranav Sandeep Dulepet:Contributed definitions for section 2 and worked on segmentation and object detection in the multimodal section.•HyoJung Han:Contributed to the Multimodal section, especially the speech+text part, and wrote the audio prompting section.•Hudson Tao:Authored sections on image, video, and 3D within multimodal, reviewed papers for human review; maintained GitHub codebase, and built the project website.•Amanda Liu:Authored taxonomic ontology sections, conducted background research for introduction and related work, developed code pipelines for meta-analysis graphs•Sweta Agrawal:Team lead for evaluation section.•Saurav Vidyadhara:Assisted with general review and revising taxonomy trees.•Chau Pham:Assisted with meta review, including automated analysis of topics.Multilingual Prompting and Meta Analysis•Zoey Ki:Led the Multilingual prompting section, conducted review on related papers, and wrote Section 3.1.•Yinheng Li:Worked on section 2.2 text-based techniques, reviewed techniques, and contributed to drafting figure 2.2.•Saloni Gupta:Wrote tests for paper compilation, helped set up paper pipeline, and worked on the code diagram and grammar for the paper.•Gerson Kroiz:Involved with section 1.1 and defining a prompt.•Aayush Gupta:Contributed to the Meta Analysis, compiling papers, and generating visualization graphs.•Michael Ilie:Co-Lead Author, managed codebase, ran experiments, collected data, and helped with various sections including the PRISMA review figure and the SCS prompting case study.•Sander Schulhoff:Lead Author
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