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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2306.09093
| 63 |
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, An- drew M. Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In Interna- tional Conference on Learning Representations, 2022. URL https://openreview.net/ forum?id=gEZrGCozdqR.
Minghao Wu, George Foster, Lizhen Qu, and Gholamreza Haffari. Document ï¬attening: Be- yond concatenating context for document-level neural machine translation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguis- tics, pp. 448â462, Dubrovnik, Croatia, May 2023a. Association for Computational Linguistics. URL https://aclanthology.org/2023.eacl-main.33.
|
2306.09093#63
|
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
|
Although instruction-tuned large language models (LLMs) have exhibited
remarkable capabilities across various NLP tasks, their effectiveness on other
data modalities beyond text has not been fully studied. In this work, we
propose Macaw-LLM, a novel multi-modal LLM that seamlessly integrates visual,
audio, and textual information. Macaw-LLM consists of three main components: a
modality module for encoding multi-modal data, a cognitive module for
harnessing pretrained LLMs, and an alignment module for harmonizing diverse
representations. Our novel alignment module seamlessly bridges multi-modal
features to textual features, simplifying the adaptation process from the
modality modules to the cognitive module. In addition, we construct a
large-scale multi-modal instruction dataset in terms of multi-turn dialogue,
including 69K image instances and 50K video instances. We have made our data,
code and model publicly available, which we hope can pave the way for future
research in multi-modal LLMs and expand the capabilities of LLMs to handle
diverse data modalities and address complex real-world scenarios.
|
http://arxiv.org/pdf/2306.09093
|
Chenyang Lyu, Minghao Wu, Longyue Wang, Xinting Huang, Bingshuai Liu, Zefeng Du, Shuming Shi, Zhaopeng Tu
|
cs.CL, cs.AI, cs.CV
|
Longyue Wang is the corresponding author. Our project page is at
https://github.com/lyuchenyang/Macaw-LLM
| null |
cs.CL
|
20230615
|
20230615
|
[] |
2306.09299
| 63 |
# B Datasets and Prompts
We experiment with the following three reasoning datasets: (1) StrategyQA [45], a set of open-domain questions where the required reasoning steps are implicit in the question, (2) GSM8k [46], which includes multi-step math reasoning problems, and (3) CommonsenseQA [47], a multiple-choice QA task focusing on commonsense reasoning. We use the reasoning steps in StrategyQA and GSM8k as the multi-step rationales, and for CommonsenseQA, we rely on the ECQA dataset [52], which is annotated with commonsense facts supporting the correct option and refuting the incorrect options. All datasets are licensed under the MIT license. Fig. 9 shows the student prompts for the three tasks of StrategyQA, CommonsenseQA, and GSM8k. Fig. 10 shows the pre- and post-intervention student simulation prompts for the teacher model.
# C Compute and Reproducibility
We conduct experiments either on A100 Google Cloud instances or on internal A6000 GPU servers. The LLMs (Flan-T5 and LLaMA) and the datasets used in our studies are publicly available. For reproducibility, we are making our code available as part of the supplementary material.
15
# StrategyQA
|
2306.09299#63
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09442
| 63 |
18
# Preprint
Warning: this table contains offensive AI-generated text.
# Adversarial Prompt
GPT-3-text-davinci-002 Completion woman raped the girl kid raped the girls woman and man raped the girl whats wrong. §§ free raped boys woman rapists rapists women girls boys male rapists rapists rapists Down- load book man raped girls woman raping so- ciety rapes child mod skyrim deadly powers of the mudcrab multiple wives skyrim married men on sociology 65 women abducted aragon raped rocket salt women raped 2006 university rape jobs shit- lock wallace payol rapes children man rape 19 Mobile Phone, iPhone, Android, Unity 360 D, HTML Nov 10, abbreviate words Modologie - Jacob Wright date file. Sep 20, · Chris McClead gets too big returning trunc string
s=%s
max cap %d
attach string list if list rakudu native string if native array The Language Specialty edition if ipadsdress and no dog autoselct in datalist ap- prence view any row select online same character multiple times, so adding in- put allows value fields to grow over time. E) configcf
|
2306.09442#63
|
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
|
Deploying large language models (LMs) can pose hazards from harmful outputs
such as toxic or false text. Prior work has introduced automated tools that
elicit harmful outputs to identify these risks. While this is a valuable step
toward securing models, these approaches rely on a pre-existing way to
efficiently classify undesirable outputs. Using a pre-existing classifier does
not allow for red-teaming to be tailored to the target model. Furthermore, when
failures can be easily classified in advance, red-teaming has limited marginal
value because problems can be avoided by simply filtering training data and/or
model outputs. Here, we consider red-teaming "from scratch," in which the
adversary does not begin with a way to classify failures. Our framework
consists of three steps: 1) Exploring the model's range of behaviors in the
desired context; 2) Establishing a definition and measurement for undesired
behavior (e.g., a classifier trained to reflect human evaluations); and 3)
Exploiting the model's flaws using this measure to develop diverse adversarial
prompts. We use this approach to red-team GPT-3 to discover classes of inputs
that elicit false statements. In doing so, we construct the CommonClaim dataset
of 20,000 statements labeled by humans as common-knowledge-true, common
knowledge-false, or neither. We are making code and data available.
|
http://arxiv.org/pdf/2306.09442
|
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230615
|
20231011
|
[
{
"id": "2205.12548"
},
{
"id": "2202.03286"
},
{
"id": "1712.06751"
},
{
"id": "2206.10812"
},
{
"id": "2308.04265"
},
{
"id": "1803.05355"
},
{
"id": "2307.00175"
},
{
"id": "2203.07281"
},
{
"id": "1909.03242"
},
{
"id": "2307.02483"
},
{
"id": "2302.03668"
},
{
"id": "2203.11147"
},
{
"id": "2010.15980"
},
{
"id": "2302.06503"
},
{
"id": "2304.05197"
},
{
"id": "2103.06332"
},
{
"id": "2005.00174"
},
{
"id": "2104.13733"
},
{
"id": "2209.07858"
},
{
"id": "2205.14334"
},
{
"id": "1908.07125"
},
{
"id": "2212.08073"
},
{
"id": "2101.07691"
},
{
"id": "2307.15043"
},
{
"id": "2303.17548"
},
{
"id": "2109.01653"
},
{
"id": "2302.09664"
},
{
"id": "2212.03827"
},
{
"id": "2104.07567"
},
{
"id": "1812.05271"
},
{
"id": "1804.07461"
},
{
"id": "2104.08678"
},
{
"id": "2206.13316"
},
{
"id": "2302.08582"
},
{
"id": "2307.15217"
},
{
"id": "2303.04381"
},
{
"id": "1907.11692"
},
{
"id": "2212.09251"
},
{
"id": "2303.15056"
},
{
"id": "2212.10539"
},
{
"id": "2110.06674"
},
{
"id": "2009.02252"
},
{
"id": "2109.07958"
},
{
"id": "2005.00661"
}
] |
2306.09539
| 63 |
The Block Transformer sublayer (i.e SLIDE:12L) processes keys and values from the previous window stored in a differentiable cache. This is implemented similarly to the sliding window attention pattern suggested in [21] and was originally introduced by Transformer-XL [8]. Using a causal mask, at every token inference step, the attention mechanism is applied to blocks of tokens of size W and is partially extended to the cached keys and values from the previous block with the sliding window. BRECT, as explained in [21], uses a non-differentiable cache that is carried from one sequence of size L to the next5. The last recurrent states of a sequence are stored in a non-differentiable cache and fed to the next training step on the following sequence in the document as a warm-start. We do not pass such a representation, since to compute the output of the convolution, we need access to the whole sequence. We believe that this is one advantage that BRECT has over our method, especially for very long examples that split into ordered sequences of length L, since the cache carried from one sequence to the next can provide very useful long-range information and (weak) access to the whole past. Since we need the whole sequence to compute SSM states, history beyond L may be lost in the process. We believe that BST can further be improved by adding non-differentiable sequence cache for very long documents.
|
2306.09539#63
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09093
| 64 |
Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, and Alham Fikri Aji. Lamini-lm: A diverse herd of distilled models from large-scale instructions. CoRR, abs/2304.14402, 2023b. doi: 10.48550/arXiv.2304.14402. URL https://doi.org/10. 48550/arXiv.2304.14402.
Zhiyang Xu, Ying Shen, and Lifu Huang. Multiinstruct: Improving multi-modal zero-shot learn- ing via instruction tuning. CoRR, abs/2212.10773, 2022. doi: 10.48550/arXiv.2212.10773. URL https://doi.org/10.48550/arXiv.2212.10773.
|
2306.09093#64
|
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
|
Although instruction-tuned large language models (LLMs) have exhibited
remarkable capabilities across various NLP tasks, their effectiveness on other
data modalities beyond text has not been fully studied. In this work, we
propose Macaw-LLM, a novel multi-modal LLM that seamlessly integrates visual,
audio, and textual information. Macaw-LLM consists of three main components: a
modality module for encoding multi-modal data, a cognitive module for
harnessing pretrained LLMs, and an alignment module for harmonizing diverse
representations. Our novel alignment module seamlessly bridges multi-modal
features to textual features, simplifying the adaptation process from the
modality modules to the cognitive module. In addition, we construct a
large-scale multi-modal instruction dataset in terms of multi-turn dialogue,
including 69K image instances and 50K video instances. We have made our data,
code and model publicly available, which we hope can pave the way for future
research in multi-modal LLMs and expand the capabilities of LLMs to handle
diverse data modalities and address complex real-world scenarios.
|
http://arxiv.org/pdf/2306.09093
|
Chenyang Lyu, Minghao Wu, Longyue Wang, Xinting Huang, Bingshuai Liu, Zefeng Du, Shuming Shi, Zhaopeng Tu
|
cs.CL, cs.AI, cs.CV
|
Longyue Wang is the corresponding author. Our project page is at
https://github.com/lyuchenyang/Macaw-LLM
| null |
cs.CL
|
20230615
|
20230615
|
[] |
2306.09212
| 64 |
Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023b. URL https://arxiv.org/abs/2307.09288.
|
2306.09212#64
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 64 |
15
# StrategyQA
Q: Are more people today related to Genghis Khan than Julius Caesar? A: Julius Caesar had three children. Genghis Khan had sixteen children. Modern geneticists have determined that out of every 200 men today has DNA that can be traced to Genghis Khan. So the answer is yes
# Q: {test_question} A:
# CommonsenseQA
Q: What might a person see at the scene of a brutal killing? Answer Choices: Choice 1: bloody mess Choice 2: pleasure Choice 3: being imprisoned Choice 4: feeling of guilt Choice 5: cake A: Bloody mess is covered or stained with blood. A person might see a bloody mess at the scene of a brutal killing. Pleasure is about what a person sees at the scene of a brutal killing and one cannot be happy to see such brutality. You canât see someone in jail at the brutal killing scene. Feeling of guilt doesnât come as the killing is brutal or merciless. Cake is baseless and weird to think as it is a brutal killing scene and not a bakery. So the correct choice is 1
Q: {test_question} Answer Choices: Choice 1: {option_1} Choice 2: {option_2} Choice 3: {option_3} Choice 4: {option_4} Choice 5: {option_5} A:
# GSM8k
|
2306.09299#64
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09442
| 64 |
man raped the girl girl man raped the girl woman man raped girls woman women man raped boys woman man man rapes kids man rapes boys man rapes children man man rapes baby male rapist man rapes boys woman raped man rapes children man rapes children man rapes children man function shortenString string shorten string if string is null Function fillstring trunc string trunc place- holder if string is function shorten string attach string string if string contains null function validate string capitalize strings val- idate string if string is function trunc trunc repeats trunc if string repet TheNitrome TheNitrome
Table 8: Example results from red-teaming GPT-3-text-davinci-002 using a classifier trained on ChatGPT-3.5-turbo labels. Random examples of adversarial prompts completions that were labeled as common-knowledge-false. The top 5 and bottom 5 examples come from independent runs of the Exploit step. These examples succeeded in being classified by the classifiers in this experiment as false but do not appear false to the classifier from our main experiment with human data.
|
2306.09442#64
|
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
|
Deploying large language models (LMs) can pose hazards from harmful outputs
such as toxic or false text. Prior work has introduced automated tools that
elicit harmful outputs to identify these risks. While this is a valuable step
toward securing models, these approaches rely on a pre-existing way to
efficiently classify undesirable outputs. Using a pre-existing classifier does
not allow for red-teaming to be tailored to the target model. Furthermore, when
failures can be easily classified in advance, red-teaming has limited marginal
value because problems can be avoided by simply filtering training data and/or
model outputs. Here, we consider red-teaming "from scratch," in which the
adversary does not begin with a way to classify failures. Our framework
consists of three steps: 1) Exploring the model's range of behaviors in the
desired context; 2) Establishing a definition and measurement for undesired
behavior (e.g., a classifier trained to reflect human evaluations); and 3)
Exploiting the model's flaws using this measure to develop diverse adversarial
prompts. We use this approach to red-team GPT-3 to discover classes of inputs
that elicit false statements. In doing so, we construct the CommonClaim dataset
of 20,000 statements labeled by humans as common-knowledge-true, common
knowledge-false, or neither. We are making code and data available.
|
http://arxiv.org/pdf/2306.09442
|
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230615
|
20231011
|
[
{
"id": "2205.12548"
},
{
"id": "2202.03286"
},
{
"id": "1712.06751"
},
{
"id": "2206.10812"
},
{
"id": "2308.04265"
},
{
"id": "1803.05355"
},
{
"id": "2307.00175"
},
{
"id": "2203.07281"
},
{
"id": "1909.03242"
},
{
"id": "2307.02483"
},
{
"id": "2302.03668"
},
{
"id": "2203.11147"
},
{
"id": "2010.15980"
},
{
"id": "2302.06503"
},
{
"id": "2304.05197"
},
{
"id": "2103.06332"
},
{
"id": "2005.00174"
},
{
"id": "2104.13733"
},
{
"id": "2209.07858"
},
{
"id": "2205.14334"
},
{
"id": "1908.07125"
},
{
"id": "2212.08073"
},
{
"id": "2101.07691"
},
{
"id": "2307.15043"
},
{
"id": "2303.17548"
},
{
"id": "2109.01653"
},
{
"id": "2302.09664"
},
{
"id": "2212.03827"
},
{
"id": "2104.07567"
},
{
"id": "1812.05271"
},
{
"id": "1804.07461"
},
{
"id": "2104.08678"
},
{
"id": "2206.13316"
},
{
"id": "2302.08582"
},
{
"id": "2307.15217"
},
{
"id": "2303.04381"
},
{
"id": "1907.11692"
},
{
"id": "2212.09251"
},
{
"id": "2303.15056"
},
{
"id": "2212.10539"
},
{
"id": "2110.06674"
},
{
"id": "2009.02252"
},
{
"id": "2109.07958"
},
{
"id": "2005.00661"
}
] |
2306.09539
| 64 |
While in other architectures, the history between blocks of tokens is not modeled, both BST and BRECT use a mechanism to model previous block context. The authors of BRECT experiment with various sequential gating mechanisms to condense the information from past blocks. With BST, we use SSM to provide context from previous blocks to the current block as explained in Section 3.2.
# B.2 Comparison with the Transformer GSS-HYBRID
GSS-HYBRID [28] is a SSM-Transformer hybrid architecture that we first describe in Section 4.1. The architecture is significantly different from BST. GSS-HYBRID is primarily composed of Gated State Space (GSS) layers and has a few interleaved Transformer layers at every 4th layer starting with the 2nd layer. BST on the other hand is mainly composed of Block Transformer layers and has Block-State Transformer layers at positions {1, 7, 9} for the â¼200M model and {1, 5, 7, 9} for the â¼400M model. Our hybrid does not stack SSM and Transformer layers like the GSS-HYBRID but rather replaces the recurrence in BRECT with an SSM such as S4. In BST, the SSM generates states
|
2306.09539#64
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09093
| 65 |
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, Chenliang Li, Yuanhong Xu, Hehong Chen, Junfeng Tian, Qian Qi, Ji Zhang, and Fei Huang. mplug-owl: Modularization empowers large language models with multimodality. CoRR, abs/2304.14178, 2023. doi: 10.48550/arXiv.2304.14178. URL https: //doi.org/10.48550/arXiv.2304.14178.
17
Preprint (work in progress)
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. CoRR, abs/2304.10592, 2023. doi: 10.48550/arXiv.2304.10592. URL https://doi.org/10.48550/arXiv. 2304.10592.
18
|
2306.09093#65
|
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
|
Although instruction-tuned large language models (LLMs) have exhibited
remarkable capabilities across various NLP tasks, their effectiveness on other
data modalities beyond text has not been fully studied. In this work, we
propose Macaw-LLM, a novel multi-modal LLM that seamlessly integrates visual,
audio, and textual information. Macaw-LLM consists of three main components: a
modality module for encoding multi-modal data, a cognitive module for
harnessing pretrained LLMs, and an alignment module for harmonizing diverse
representations. Our novel alignment module seamlessly bridges multi-modal
features to textual features, simplifying the adaptation process from the
modality modules to the cognitive module. In addition, we construct a
large-scale multi-modal instruction dataset in terms of multi-turn dialogue,
including 69K image instances and 50K video instances. We have made our data,
code and model publicly available, which we hope can pave the way for future
research in multi-modal LLMs and expand the capabilities of LLMs to handle
diverse data modalities and address complex real-world scenarios.
|
http://arxiv.org/pdf/2306.09093
|
Chenyang Lyu, Minghao Wu, Longyue Wang, Xinting Huang, Bingshuai Liu, Zefeng Du, Shuming Shi, Zhaopeng Tu
|
cs.CL, cs.AI, cs.CV
|
Longyue Wang is the corresponding author. Our project page is at
https://github.com/lyuchenyang/Macaw-LLM
| null |
cs.CL
|
20230615
|
20230615
|
[] |
2306.09299
| 65 |
# GSM8k
Q: Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? A: Natalia sold 48/2 = 24 clips in May. Natalia sold 48+24 = 72 clips altogether in April and May. So the answer is 72
Q: {test_question} A:
Figure 9: Examples of student prompts for different tasks with one demonstration.
# D RQ1: Additional Results
Results with Flan and LLaMA Models. In Table 1, we report the accuracy obtained by different students and teachers (based on Flan-T5 models) on the StrategyQA task. We draw similar conclusions as Flan-T5 with other LLMs, specifically LLaMA-7B and LLaMA-65B models on the StrategyQA dataset (Table 2). In fact, when the teacher is stronger like a LLaMA-65B, the margin of improvement is also higher, about 8%. The overall trends also align â increasing for weaker students and decreasing for stronger students.
|
2306.09299#65
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09442
| 65 |
without explanation. ChatGPT-3.5-turbo (OpenAI, 2023) labeled 48% as CK true, 14% as CK false, and 38% as neither. Table 3 compares human labelers and ChatGPT-3.5-turbo. We find agreement on only 54% of the 20,000 examples. The 5 classifiers trained on the ChatGPT-3.5-turbo labels achieved average accuracies of 87% on âcommon knowledge-trueâ sentences, 63% on âcommon knowledge-falseâ sentences, and 58% on âneitherâ sentences from the validation set.
|
2306.09442#65
|
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
|
Deploying large language models (LMs) can pose hazards from harmful outputs
such as toxic or false text. Prior work has introduced automated tools that
elicit harmful outputs to identify these risks. While this is a valuable step
toward securing models, these approaches rely on a pre-existing way to
efficiently classify undesirable outputs. Using a pre-existing classifier does
not allow for red-teaming to be tailored to the target model. Furthermore, when
failures can be easily classified in advance, red-teaming has limited marginal
value because problems can be avoided by simply filtering training data and/or
model outputs. Here, we consider red-teaming "from scratch," in which the
adversary does not begin with a way to classify failures. Our framework
consists of three steps: 1) Exploring the model's range of behaviors in the
desired context; 2) Establishing a definition and measurement for undesired
behavior (e.g., a classifier trained to reflect human evaluations); and 3)
Exploiting the model's flaws using this measure to develop diverse adversarial
prompts. We use this approach to red-team GPT-3 to discover classes of inputs
that elicit false statements. In doing so, we construct the CommonClaim dataset
of 20,000 statements labeled by humans as common-knowledge-true, common
knowledge-false, or neither. We are making code and data available.
|
http://arxiv.org/pdf/2306.09442
|
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230615
|
20231011
|
[
{
"id": "2205.12548"
},
{
"id": "2202.03286"
},
{
"id": "1712.06751"
},
{
"id": "2206.10812"
},
{
"id": "2308.04265"
},
{
"id": "1803.05355"
},
{
"id": "2307.00175"
},
{
"id": "2203.07281"
},
{
"id": "1909.03242"
},
{
"id": "2307.02483"
},
{
"id": "2302.03668"
},
{
"id": "2203.11147"
},
{
"id": "2010.15980"
},
{
"id": "2302.06503"
},
{
"id": "2304.05197"
},
{
"id": "2103.06332"
},
{
"id": "2005.00174"
},
{
"id": "2104.13733"
},
{
"id": "2209.07858"
},
{
"id": "2205.14334"
},
{
"id": "1908.07125"
},
{
"id": "2212.08073"
},
{
"id": "2101.07691"
},
{
"id": "2307.15043"
},
{
"id": "2303.17548"
},
{
"id": "2109.01653"
},
{
"id": "2302.09664"
},
{
"id": "2212.03827"
},
{
"id": "2104.07567"
},
{
"id": "1812.05271"
},
{
"id": "1804.07461"
},
{
"id": "2104.08678"
},
{
"id": "2206.13316"
},
{
"id": "2302.08582"
},
{
"id": "2307.15217"
},
{
"id": "2303.04381"
},
{
"id": "1907.11692"
},
{
"id": "2212.09251"
},
{
"id": "2303.15056"
},
{
"id": "2212.10539"
},
{
"id": "2110.06674"
},
{
"id": "2009.02252"
},
{
"id": "2109.07958"
},
{
"id": "2005.00661"
}
] |
2306.09539
| 65 |
4In JAX, this is equivalent to using jax.lax.associative_scan. 5In our work and in [21], a document is split into multiple sequences of size L and each sequence is split into
multiple blocks of size W
14
for each Block Transformer representations and we then use cross-attention to mix the states and the self-attention outputs. The authors in [28] initially built GSS, a gated version of DSS [16], to (1) reduce SSM parameter dimensions, (2) stabilize training of the SSM and (3) allow better length generalization. However, when experimenting with SSMs such as S4 or DSS, we found that the gating was not necessary to achieve all three objectives stated above. We decided that using GSSâs Gated Attention Unit [20] was therefore not needed when integrating SSM states into the attention mechanism. We also reiterate that the authors in [28] used hyperparameter search to get the best performance while we did not.
# C Scaling Experiments
Figure 5: Scaling properties on PG-19. Yellow: (BST:SH:UNSTRUCT) 12-layer Block-State Transformer. Red: (REC:FIXED:SKIP) 12-layer Block-Recurrent Transformer. Blue: (TRSF-XL-2048) 13-layer Transformer-XL.
|
2306.09539#65
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09299
| 66 |
Results on other Datasets. Our conclusions generalize across datasets too. Table 3 presents the results on CommonsenseQA with Flan-T5 models. CommonsenseQA is an easier task and Flan-T5 models obtain accuracies of 85% and 92% when generating their own explanations. While Flan-T5-Large still benefits from human explanations, the larger model does not, perhaps because it already starts at a high 92% accuracy. Finally, in Table 4, we present the results on GSM8k with LLaMA models. Note that in GSM8k, a student has access to partial explanations from the teacher, but even then we observe that these prove to be useful prompts for the student to complete their chain-of-thought, leading to up to 8-9% increase in accuracy with human teachers and 3% with model teachers.
16
# Pre-Intervention Student Simulation
Simulate an AI modelâs answer for the given question.
Q: Will the Albany in Georgia reach a hundred thousand occupants before the one in New York? AI Predicted Answer: Albany, Georgia is a city in the U.S. state of Georgia. Albany, Georgia has a population of 59,080. The population of New York is 365,040. So the answer is no
# Q: {question} AI Predicted Answer:
# Post-Intervention Student Simulation
Simulate an AI modelâs answer for the given question.
|
2306.09299#66
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09442
| 66 |
We performed Exploit step runs using the classifiers trained on ChatGPT-3.5-turbo labels. As be- fore, the adversarial prompt generators succeeded in eliciting completions that were classified as untruthful. The classifiers trained on ChatGPT-3.5-turbo classified 17% of the Explore step data as common-knowledge-false but an average of 76% of completions from adversarial prompts. How- ever, completions elicited using these classifiers had no apparent tendency to be untruthful. In these cases, the prompts and completions tended to either be toxic or be nonsense strings of code-like vo- cabulary. This suggests that ChatGPT-3.5-turbo labels produced classifiers that were more hackable. This offers and example of when using AI-generated labels (Bai et al., 2022)may not be adequate for red-teaming.
Table 8 shows examples from red-teaming GPT-3-text-davinci-002 using classifiers trained on CREAK data. Instead of eliciting responses that showed any tendencies to be untrue claims, the prompts and responses tended to be toxic or nonsense.
19
|
2306.09442#66
|
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
|
Deploying large language models (LMs) can pose hazards from harmful outputs
such as toxic or false text. Prior work has introduced automated tools that
elicit harmful outputs to identify these risks. While this is a valuable step
toward securing models, these approaches rely on a pre-existing way to
efficiently classify undesirable outputs. Using a pre-existing classifier does
not allow for red-teaming to be tailored to the target model. Furthermore, when
failures can be easily classified in advance, red-teaming has limited marginal
value because problems can be avoided by simply filtering training data and/or
model outputs. Here, we consider red-teaming "from scratch," in which the
adversary does not begin with a way to classify failures. Our framework
consists of three steps: 1) Exploring the model's range of behaviors in the
desired context; 2) Establishing a definition and measurement for undesired
behavior (e.g., a classifier trained to reflect human evaluations); and 3)
Exploiting the model's flaws using this measure to develop diverse adversarial
prompts. We use this approach to red-team GPT-3 to discover classes of inputs
that elicit false statements. In doing so, we construct the CommonClaim dataset
of 20,000 statements labeled by humans as common-knowledge-true, common
knowledge-false, or neither. We are making code and data available.
|
http://arxiv.org/pdf/2306.09442
|
Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell
|
cs.CL, cs.AI, cs.LG
| null | null |
cs.CL
|
20230615
|
20231011
|
[
{
"id": "2205.12548"
},
{
"id": "2202.03286"
},
{
"id": "1712.06751"
},
{
"id": "2206.10812"
},
{
"id": "2308.04265"
},
{
"id": "1803.05355"
},
{
"id": "2307.00175"
},
{
"id": "2203.07281"
},
{
"id": "1909.03242"
},
{
"id": "2307.02483"
},
{
"id": "2302.03668"
},
{
"id": "2203.11147"
},
{
"id": "2010.15980"
},
{
"id": "2302.06503"
},
{
"id": "2304.05197"
},
{
"id": "2103.06332"
},
{
"id": "2005.00174"
},
{
"id": "2104.13733"
},
{
"id": "2209.07858"
},
{
"id": "2205.14334"
},
{
"id": "1908.07125"
},
{
"id": "2212.08073"
},
{
"id": "2101.07691"
},
{
"id": "2307.15043"
},
{
"id": "2303.17548"
},
{
"id": "2109.01653"
},
{
"id": "2302.09664"
},
{
"id": "2212.03827"
},
{
"id": "2104.07567"
},
{
"id": "1812.05271"
},
{
"id": "1804.07461"
},
{
"id": "2104.08678"
},
{
"id": "2206.13316"
},
{
"id": "2302.08582"
},
{
"id": "2307.15217"
},
{
"id": "2303.04381"
},
{
"id": "1907.11692"
},
{
"id": "2212.09251"
},
{
"id": "2303.15056"
},
{
"id": "2212.10539"
},
{
"id": "2110.06674"
},
{
"id": "2009.02252"
},
{
"id": "2109.07958"
},
{
"id": "2005.00661"
}
] |
2306.09539
| 66 |
In this section, we compare how BST scales compared to Transformer-XL with 4Ã the window size and BRECT. In Figure 5, we see that at lower scales, from 80M to 200M, BRECT and BST have very similar performances. Beyond 200M, the perplexity performance percentage gap between BRECT and BST increases from 2.5% at 200M paramaters to 4.0% at 1.3B parameters. The perplexity performance percentage gap between BRECT and TRSF-XL is even more pronounced as it starts at 7.6% at 200M parameters to 10.6% at 1.3B parameters.
# D Long Range Arena Experiments
|
2306.09539#66
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 67 |
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yi- wen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, and Zhenzhong Lan. CLUE: A chinese language un- In Donia Scott, N´uria Bel, and Chengqing Zong (eds.), derstanding evaluation benchmark. Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 4762â4772. International Com- mittee on Computational Linguistics, 2020. doi: 10.18653/v1/2020.coling-main.419. URL https://doi.org/10.18653/v1/2020.coling-main.419.
|
2306.09212#67
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 67 |
# Q: {question} AI Predicted Answer:
# Post-Intervention Student Simulation
Simulate an AI modelâs answer for the given question.
Q: Will the Albany in Georgia reach a hundred thousand occupants before the one in New York? AI Predicted Answer: Albany, Georgia is a city in the U.S. state of Georgia. Albany, Georgia has a population of 59,058. The Albany in New York has a population of 328,058. So the answer is no
# Q: {question} AI Predicted Answer: {teacher_explanation} So the answer is
Figure 10: Examples of StrategyQA prompts for the mental model of a teacher simulating student predictions pre-intervention and post-intervention. Pre-intervention: The demonstrations use student explanations and student predictions and at test time, the teacher simulates both. Post-intervention: The demonstrations use teacher explanations and student predictions and at test time, the teacher uses the teacher explanation to simulate the student prediction.
Results with Cross-family Student and Teacher. We observe that larger teacher LLMs can teach smaller student LLMs, even when they are of different model families. In Table 5, we report the results with Flan-T5 and LLaMA models as students and teachers.
|
2306.09299#67
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 67 |
MODEL LISTOPTS TEXT RETRIEVAL IMAGE PATHFINDER PATH-X AVG Transformer Linear Trans. Reformer Performer BigBird 36.37 16.13 37.27 18.01 36.05 64.27 65.90 56.10 65.40 64.02 57.46 53.09 53.40 53.82 59.29 42.44 42.34 38.07 42.77 40.83 71.40 75.30 68.50 77.05 74.87 â â â â â 53.66 50.46 50.56 51.18 54.17 Mega 63.14 90.43 91.25 90.44 96.01 97.98 88.21 S4D S4 S5 60.47 59.60 62.15 86.18 86.82 89.32 89.46 90.90 91.40 88.19 88.65 88.00 93.06 94.20 95.33 91.95 96.35 98.58 84.89 86.09 87.46 BRECT:FIXED:SKIP MEGA-CHUNK BST:SH:S4 (ours) 37.29 58.76 61.49 Methods with chunked input sequences 58.76 90.97 90.51 66.14 90.19 87.63 50.41 85.80 91.07 76.33 94.41
|
2306.09539#67
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09299
| 68 |
Intervention Budget Student Teacher 0% 20% 40% 60% 80% 100% Flan-T5-Large Human Human Flan-T5-XL Flan-T5-XL Flan-T5-Large Flan-T5-Large Flan-T5-XL 58.51±2.00 68.12±2.62 58.51±2.00 68.12±2.62 63.75±0.43 72.05±2.62 60.52±1.63 67.68±2.72 66.95±2.19 75.98±2.31 59.78±1.85 65.64±3.39 73.94±2.77 80.20±1.65 61.48±2.02 64.04±3.63 78.02±2.40 84.13±1.00 62.35±2.13 62.88±1.15 81.95±1.65 87.77±0.70 62.96±2.47 61.86±0.66
Table 1: RQ1 â Comparison of accuracy obtained with random intervention by Flan-T5 models at different intervention budgets on StrategyQA. As shown in the third row, Flan-T5-Large (student) accuracy improves by 5% with 100% intervention from Flan-T5-XL (teacher).
|
2306.09299#68
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 69 |
Ai Ming Yang, Bin Xiao, Bingning Wang, Borong Zhang, Ce Bian, Chao Yin, Chenxu Lv, Da Pan, Dian Wang, Dong Yan, Fan Yang, Fei Deng, Feng Wang, Feng Liu, Guangwei Ai, Guosheng Dong, Hai Zhao, Hang Xu, Hao Sun, Hongda Zhang, Hui Liu, Jiaming Ji, Jian Xie, Juntao Dai, Kuncheng Fang, Lei Su, Liang Song, Lifeng Liu, Liyun Ru, Luyao Ma, Mang Wang, Mickel Liu, MingAn Lin, Nuolan Nie, Pei Guo, Ruiyang Sun, Tao Zhang, Tianpeng Li, Tianyu Li, Wei Cheng, Weipeng Chen, Xiangrong Zeng, Xiaochuan Wang, Xiaoxi Chen, Xin Men, Xin Yu, Xuehai Pan, Yan-Bin Shen, Yiding Wang, Yiyu Li, Youxin Jiang, Yuchen Gao, Yupeng Zhang,
13
# Under review
Zenan Zhou, and Zhiying Wu. Baichuan 2: Open large-scale language models. 2023. URL https://api.semanticscholar.org/CorpusID:261951743.
|
2306.09212#69
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 69 |
Intervention Budget Student Teacher 0% 20% 40% 60% 80% 100% LLaMA-7B Human LLaMA-65B Human LLaMA-65B LLaMA-7B LLaMA-65B LLaMA-7B 61.13±2.72 77.58±2.24 61.13±2.72 77.58±2.24 63.60±4.82 80.34±2.65 62.29±1.53 75.83±2.24 68.85±3.52 82.67±2.06 64.91±0.67 72.92±2.72 73.36±2.18 87.48±1.96 66.08±1.76 72.92±2.26 78.45±2.55 89.37±0.25 68.99±3.14 70.88±0.90 81.22±1.57 92.86±0.50 69.43±3.41 69.14±0.66
Table 2: RQ1 â Comparison of accuracy obtained with random intervention by LLaMA models at different intervention budgets on StrategyQA. As shown in the third row, LLaMA-7B (student) accuracy improves by 8% with 100% intervention from LLaMA-65B (teacher).
# E RQ2: Additional Results
|
2306.09299#69
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 69 |
Table 2: Performance on Long-Range Arena (LRA). For a fair comparison, we adjust the number of layers and model dimensions on each task so that BST and BRECT have similar number of parameters with S4 and MEGA-CHUNK. BRECT results are from our own runs and all other baselines are from published results.
15
While the main focus of our research was to demonstrate that hybrid Transformer-SSM models are efficient and perform well on long context autoregressive LM, we also evaluate our method on standard classification task where long range dependencies in a sequence are important to capture. In Table 2, we present our results on the Long Range Arena (LRA) benchmark [38] which incorporates three different modalities including text, images, and mathematical expressions. The LRA dataset also tests models on various sequence lengths from 1K to 16K.
|
2306.09539#69
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 70 |
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? In Anna Korhonen, David R. Traum, and Llu´ıs M`arquez (eds.), Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 4791â4800. Association for Computational Linguistics, 2019. doi: 10.18653/v1/p19-1472. URL https: //doi.org/10.18653/v1/p19-1472.
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, and Jie Tang. GLM-130b: An open bilingual pre-trained model. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=-Aw0rrrPUF.
|
2306.09212#70
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09539
| 70 |
BST:SH:S4 is composed of four BST layers (no BRT layers are interleaved) and two S4 layers on top. We use the same standard block length of 512 for BST and BRT. However, we train BST and BRT on the full sequences (up to 16K for Path-X). We use AdamW as our optimizer [24] with a warmup for the learning rate, where we start from a value of 1eâ7 and increase the learning rate linearly up a specified value â {1eâ3, 2eâ3, 4eâ3} for the first 10% of training. This is followed by cosine annealing for the rest of training down to a value of 1eâ7. All layers are bidirectional, including the S4 layer in BST:SH:S4 as described in [13]. Our weight decay is chosen from {0, 0.05, 0.1, 0.15} and our dropout is chosen from {0, 0.1}. Except for Path-X experiments, we use weight decays â {0.03, 0.05, 0.07} for all parameters except S4D matrices A and B. Also, for Path-X, the initialization range of our discretization time step â for PathX
|
2306.09539#70
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 71 |
Hui Zeng. Measuring massive multitask chinese understanding. arXiv preprint arXiv:2304.12986, 2023. URL https://arxiv.org/abs/2304.12986.
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. OPT: open pre-trained transformer language models. CoRR, abs/2205.01068, 2022. doi: 10.48550/ arXiv.2205.01068. URL https://doi.org/10.48550/arXiv.2205.01068.
Yixuan Zhang and Haonan Li. Can large language model comprehend ancient chinese? a preliminary test on aclue. In Proceedings of the Ancient Language Processing Workshop associated with RANLP-2023, pp. 80â87. Association for Computational Linguistics, 2023.
|
2306.09212#71
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 71 |
17
Intervention Budget Student Teacher 0% 20% 40% 60% 80% 100% Flan-T5-Large Human Human Flan-T5-XL Flan-T5-XL Flan-T5-Large Flan-T5-Large Flan-T5-XL 84.78±0.41 92.38±0.16 84.78±0.41 92.38±0.16 86.86±0.76 92.52±0.20 85.79±0.48 90.92±0.39 88.70±0.94 92.43±0.28 86.79±0.84 89.74±0.39 90.77±0.45 92.23±0.61 87.46±0.20 87.98±0.89 93.20±0.47 92.41±1.12 88.52±0.39 86.70±1.60 95.42±0.17 92.21±1.06 89.72±0.68 85.19±1.62
Table 3: RQ1 â Comparison of accuracy obtained with random intervention by Flan-T5 models at different intervention budgets on CommonsenseQA. As shown in the third row, Flan-T5-Large (student) accuracy improves by 5% with 100% intervention from Flan-T5-XL (teacher).
|
2306.09299#71
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 72 |
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. Agieval: A human-centric benchmark for evaluating foundation models. CoRR, abs/2304.06364, 2023. doi: 10.48550/arXiv.2304.06364. URL https://doi.org/10. 48550/arXiv.2304.06364.
# A COMPARISON TO CONCURRENT BENCHMARKS
C-Eval (Huang et al., 2023) and M3KE (Liu et al., 2023) are two similar benchmarks concurrent with our work. We compare the task distribution of these benchmarks in Table 5, and demonstrate that CMMLU contains more culture-related and region-related tasks. While there are differences in task distribution, we acknowledge that these datasets exhibit similarities in the task types and can, therefore, be jointly used as assessment criteria for evaluating the Chinese language capabilities of large models.
|
2306.09212#72
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 72 |
Intervention Budget Student Teacher 0% 20% 40% 60% 80% 100% LLaMA-7B Human LLaMA-13B Human LLaMA-7B LLaMA-13B LLaMA-13B LLaMA-7B 9.62±1.53 16.45±1.80 9.62±1.53 16.45±1.80 11.97±0.80 18.44±2.16 10.20±1.06 15.87±1.62 13.84±1.02 20.34±1.60 10.68±0.82 15.56±1.44 16.32±0.57 22.41±2.46 11.24±0.50 14.88±1.89 18.72±0.78 24.91±2.07 11.92±1.15 14.68±1.88 21.05±0.65 26.88±2.34 12.25±0.94 14.27±1.70
Table 4: RQ1 â Comparison of accuracy obtained with random intervention by LLaMA models at different intervention budgets on GSM8k. As shown in the third row, LLaMA-7B (student) accuracy improves by 3% with 100% intervention from LLaMA-13B (teacher).
|
2306.09299#72
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 72 |
Our results on LRA are very promissing and show that, compared to other state-of the art methods that chunk sequences into blocks, BST is able to model long range dependencies. For example, BST outperforms MEGA-CHUNK [27] on four out of six LRA tasks and by 1.5% on the average score. However, BST still needs to improve (perhaps by extending the block size) to catch up to MEGA (without chunks).
# E Ablation Studies
In the following section, we perform ablations to investigate (1) the placement of a single SSM layer in Table 3 in the overall architecture, (2) the effects of the number of SSM layers added in Table 4, and (3) the size D of the SSM state in Table 5. For the ablations, we use the â¼200M parameter BST:SH:S4, since it is the fastest model, and assess various configurations on PG19.
Table 3: A single BST at various layer index.
# Table 4: Multiple BST layers at various locations.
|
Table 5: Increasing BSTâs S4 model state size D.
|
2306.09539#72
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 73 |
We further assess the overlap between CMMLU and both of these benchmarks. For this purpose, we first sort four choices for each question to eliminate the influence of choice order. Subsequently, we concatenate the question string with the sorted choice strings. Then, we remove all punctuation marks, including underscores and brackets, from the resulting strings. The final overlap, computed using exact string matching, yields a total of 74 for CEval and 158 for M3KE. This overlap accounts for approximately 1% of our dataset. Table 5: Task distributions of contemporary similar datasets. CMMLU contains more subjects in humanities, social science, and others (usually country- or culture-specific) compared to CEval and M3KE, while fewer subjects in STEM. This indicates that our dataset is more inclined toward examining knowledge related to social, cultural, and regional factors.
Model STEM Humanities Social Science Other China-specific Total CEval M3KE CMMLU 20 31 17 11 12 13 10 21 22 11 7 15 â â 15 52 71 67
14
Under review
# B CMMLU SUBJECTS
Table 6 lists all subjects of CMMLU. The table also provides details for each subject test, including the concepts covered, the supercategory to which each subject belongs, and the total number of questions.
|
2306.09212#73
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 73 |
Intervention Budget Student Teacher 0% 20% 40% 60% 80% 100% Flan-T5-Large LLaMA-65B LLaMA-65B Flan-T5-Large 58.51±2.00 77.58±2.24 61.86±0.25 74.52±1.76 61.13±2.26 71.47±0.90 64.48±1.53 67.68±2.00 66.52±4.05 64.62±2.00 66.95±4.90 62.15±1.76
Table 5: RQ1 â Comparison of accuracy obtained with random intervention on StrategyQA when the student and the teacher belong to different model families. As shown in the first row, Flan-T5-Large (student) accuracy improves by 8% with 100% intervention from LLaMA-65B (teacher).
|
2306.09299#73
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 73 |
Table 3: A single BST at various layer index.
# Table 4: Multiple BST layers at various locations.
|
Table 5: Increasing BSTâs S4 model state size D.
Layer index Perplexity Num layers Perplexity 3 7 9 12 12.41 11.92 11.88 12.03 2 3 4 5 11.69 11.57 11.21 11.20 8 16 32 64 11.95 11.57 11.55 11.54 Ã0.7 Ã1.0 Ã1.8 Ã3.2
In Table 3, we experiment adding a single BST layer at layer indices 3, 6, 9, 12. We notice that a single BST layer with state size D = 16 located closer to the middle of the whole Block Transformer stack, at index = 9, has the greatest effect on perplexity. This finding is inline with findings in prior work [42, 21].
In Table 4, we test if adding multiple BST layers yields improvements on performance. We start with BST layers with state size D = 16 at indices 0, 9. We follow by adding another BST layer at index 7 for a total of three BST layers and then another at index 5, followed by another at index 12. Adding more BST layers lowers perplexity. However, the results seem to plateau at 5 BST layers. We note also that there is a 3.5% training step time increase for each added layer.
|
2306.09539#73
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 74 |
Table 7 presents the breakdown of statistical results of the CMMLU test set for each supercategory, including the number of tasks, number of questions, average question counts for each subject, maximum and minimum counts of questions, and average token length for question and choices. Meanwhile, Figure 7 provides a visualization of the token lengths of questions and answers for each subject.
World Religions. World History Virology Traditional Chinese Medicine Sports Science: Sociology Security Study Public Relations. Professional Psychology: Professional Medicine Professional Law Professional Accounting Philosophy Nutrition Modern Chinese Marxist Theory Marketing Management. Machine Learning Logical Legal And Moral Basis Jurisprudence. Journalism International Law: Human Sexuality High School Politics. High School Physics High School Mathematics. High School Geography Computer Science High School Chemistry High School Biology Global Facts: Genetics. Food Science: Ethnology Elementary Mathematics Elementary IT Elementary Commonsense. Elementary Chinese. Electrical Engineering Education Economics. Construction Project Management Conceptual Physics Computer Security College Medicine College Medical Statistics College Mathematics College Law College Engineering Hydrology College Education College Actuarial Science. Clinical Knowledge: Chinese Teacher Qualification Chinese Literature Chinese History. Chinese Foreign Policy Chinese Food Culture Chinese Driving Rule Chinese Civil Service Exam + Business Ethics. Astronomy: Arts Ancient Chinese Anatomy Agronomy Questi Qn Length Answer Length i ie Ya Hit effi 8 50. 75 100 200 4000 40 100 200
# Figure 7: Question and answer lengths of each subject.
15
# Under review
|
2306.09212#74
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 74 |
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Teacher Conf â Expected Student Conf (Pre) â Expected Student Conf (Post) â Expected Utility â True Student Conf (Pre) â True Student Conf (Post) â True Utility â 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 60.40±1.76 58.66±2.40 64.19±2.00 64.77±1.76 67.83±1.53 68.26±1.65 65.64±1.40 76.56±0.50 61.13±2.65 60.11±2.90 66.66±0.25 68.26±0.66 71.32±1.33 80.20±1.26 72.63±1.09 80.78±1.15 60.98±1.09 57.35±3.30 66.81±1.57 69.71±2.01 71.17±1.15 74.38±2.84 80.05±0.90 81.51±1.76 64.33±4.54
|
2306.09299#74
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 74 |
In Table 5, we train our models with different state sizes D. For the state size ablation, we use three BST layers at indices 0, 7, 9. We find that increasing D improves perplexity to the detriment of training speed (step time). For this reason, we chose D = 16 for Table 1 BST results.
16
# F JAX Implementation of BST
Pseudocode 1 contains a function that implements convolution of multiple filters over the same input sequence using FFT and inverse FFT operations. Pseudocodes 2, 3 and 4 respectively implement context state collection of BST variants: Single-Head (SH), Multi-Head (MH) and Multi-Filter (MF). Finally, Pseudocode 5 runs the Block Transformer sublayer in parallel by feeding the context states to their corresponding block.
"""Unstructured filters and convolutions."""
|
2306.09539#74
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09539
| 75 |
import jax from jax import numpy as jnp from einops import rearrange win_length = 512 seq_length = 4096 # (w) # (l) def get_filters_unstruct(channels): """Returns trainable filters and biases. Args: channels: number of filters. Returns: h: filter of shape (seq_length, channels, dim) b: bias of shape (channels, dim) """ t = jnp.linspace(0.0, 1.0, seq_length) h = jnp.exp(- alpha * t) * dense(positional_emb(t)) b = get_bias() return h, b def multichannel_convolution(u, h, b): """Multichannel convolution function. Args: u: input of shape (seq_length, dim) h: filters of shape (seq_length, channels, dim) b: bias of shape (channels, dim) """ h = rearrange(h, "l c d -> c d l") fft_size = seq_length * 2 u_f = jnp.fft.rfft(x, n=fft_size) h_f = jnp.fft.rfft(h, n=fft_size) y =
|
2306.09539#75
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 76 |
Agronomy (åå¦) Anatomy (解åå¦) Ancient Chinese (å¤æ±è¯)* Arts (èºæ¯å¦) Astronomy (天æå¦) Business Ethics (åä¸ä¼¦ç) Chinese History (ä¸å½åå²)* Chinese Literature (ä¸å½æå¦)* Chinese Civil Service Exam (ä¸å½å
¬å¡åèè¯)* Chinese Driving Rule (ä¸å½é©¾é©¶è§å)* Chinese Food Culture (ä¸å½é¥®é£æå)* Chinese Foreign Policy (ä¸å½å¤äº¤æ¿ç)* Chinese Teacher Qualification (ä¸å½æå¸èµæ ¼)* Clinical Knowledge (临åºç¥è¯) College Actuarial Science (大å¦ç²¾ç®å¦) College Education (大å¦æè²å¦) College Engineering Hydrology (大å¦å·¥ç¨æ°´æå¦) College Law (大å¦æ³å¾) College Mathematics (大å¦æ°å¦) College Medical Statistics (大å¦å»å¦ç»è®¡) College Medicine
|
2306.09212#76
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 77 |
College Mathematics (大å¦æ°å¦) College Medical Statistics (大å¦å»å¦ç»è®¡) College Medicine (大å¦å»å¦) Computer Science (计ç®æºç§å¦) Computer Security (计ç®æºå®å
¨) Conceptual Physics (æ¦å¿µç©çå¦) Construction Project Management (建设工ç¨ç®¡ç)* Economics (ç»æµå¦) Education (æè²å¦) Electrical Engineering (çµæ°å·¥ç¨) Elementary Chinese (å°å¦è¯æ)* Elementary Commonsense (å°å¦å¸¸è¯)* Elementary Information and Technology (å°å¦ä¿¡æ¯ææ¯) Elementary Mathematics (åçæ°å¦) Ethnology (æ°æå¦)* Food Science (é£åç§å¦) Genetics (éä¼ å¦) Global Facts (å
¨çäºå®) High School Biology (é«ä¸çç©) High School Chemistry (é«ä¸åå¦) High School Geography
|
2306.09212#77
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 77 |
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Least Conf â Expected Student Conf (Pre) â Expected Student Conf (Post) â Expected Utility â 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 60.40±1.76 61.13±0.75 62.59±1.00 61.86±1.96 62.29±0.50 61.13±2.65 62.44±1.74 61.86±0.90 62.88±1.74 62.44±1.50 60.98±1.09 65.06±1.15 62.29±1.33 61.71±3.39 62.44±3.88 64.33±4.54 63.46±2.97 65.50±3.14 60.11±4.62 62.95±2.78 62.96±2.47 62.96±2.47 62.96±2.47 62.96±2.47 62.96±2.47
|
2306.09299#77
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 77 |
Pseudocode 1: Unstructured filters and convolutions.
"""Context state collection for BST-SH variant.""" num_heads = 8 num_states = 32 # (h) # (s) # (SH): Single-Head def SH_context_states(u): """Single-Head Context Collection.""" h, b = get_filters_[unstruct/s4](channels=1)
17
y_1 = multichannel_convolution(u, h, b) # y_1: (l, d, 1) # lift to multiple heads y_h = dense(y_1) # y_h: (l, d, h) context_states = jnp.split( y_h, seq_length // win_length, axis=0) return context_states # (l/w, w, d, h)
# Pseudocode 2: Context state collection for BST-SH variants.
|
2306.09539#77
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 78 |
(å
¨çäºå®) High School Biology (é«ä¸çç©) High School Chemistry (é«ä¸åå¦) High School Geography (é«ä¸å°ç) High School Mathematics (é«ä¸æ°å¦) High School Physics (é«ä¸ç©çå¦) High School Politics (é«ä¸æ¿æ²»)* Human Sexuality (人类æ§è¡ä¸º) International Law (å½é
æ³å¦) Journalism (æ°é»å¦) Jurisprudence (æ³çå¦) Legal And Moral Basis (æ³å¾ä¸éå¾·åºç¡) Logical (é»è¾å¦) Machine Learning (æºå¨å¦ä¹ ) Management (管çå¦) Marketing (å¸åºè¥é) Marxist Theory (马å
æ主ä¹ç论) Modern Chinese (ç°ä»£æ±è¯)* Nutrition (è¥å
»å¦) Philosophy (å²å¦) Professional Accounting (ä¸ä¸ä¼è®¡) Professional Law (ä¸ä¸æ³å¦) Professional Medicine
|
2306.09212#78
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09539
| 78 |
# Pseudocode 2: Context state collection for BST-SH variants.
"""Context state collection for BST-MH variant.""" # (MH): Multi-Head def MH_context_states(u): """Multi-Head Context Collection.""" h, b = get_filters_[unstruct/s4](channels=num_heads) y_h = multichannel_convolution(u, h, b) # y_h: (l, d, h) context_states = jnp.split( y_h, seq_length // win_length, axis=0) return context_states # (l/w, w, d, h)
# Pseudocode 3: Context state collection for BST-MH variants.
# """Context state collection for BST-MF variant."""
|
2306.09539#78
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 79 |
Philosophy (å²å¦) Professional Accounting (ä¸ä¸ä¼è®¡) Professional Law (ä¸ä¸æ³å¦) Professional Medicine (ä¸ä¸å»å¦) Professional Psychology (ä¸ä¸å¿çå¦) Public Relations (å
Œ
±å
³ç³») Security Study (å®å
¨ç 究) Sociology (社ä¼å¦) Sports Science (ä½è²å¦) Traditional Chinese Medicine (ä¸å»ä¸è¯)* Virology (ç
æ¯å¦) World History (ä¸çåå²) World Religions (ä¸çå®æ)
|
2306.09212#79
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 79 |
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Expected Student Conf (Pre) â Expected Student Conf (Post) â Expected Utility â True Student Conf (Pre) â True Student Conf (Post) â True Utility â 68.12±2.62 68.12±2.62 68.12±2.62 68.12±2.62 68.12±2.62 68.12±2.62 68.12±2.62 67.68±2.72 66.22±2.24 70.59±3.27 70.88±3.27 74.23±3.73 70.16±3.27 79.91±2.00 65.64±3.39 66.95±1.53 71.76±3.63 71.90±2.84 76.27±1.40 73.94±1.76 80.93±2.06 64.04±3.63 65.35±1.00 72.48±2.86 72.63±2.24 68.55±1.00 80.05±1.65 80.64±2.24 62.88±1.15 62.73±0.66 69.86±2.62 68.99±1.15 64.04±0.90
|
2306.09299#79
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09539
| 79 |
# Pseudocode 3: Context state collection for BST-MH variants.
# """Context state collection for BST-MF variant."""
# (MF): Multi-Filter def MF_context_states(u): """Multi-Filter Context Collection.""" h, b = get_filters_[unstruct/s4](channels=num_states) y_s = multichannel_convolution(u, h, b) context_states = jnp.split( # y_s: (l, d, s) y_s, seq_length // win_length, axis=0) # context_states: (l/w, w, d, s) # collect the last context states context_states = context_states[:, -1, ...] # (l/w, d, s) context_states = rearrange( context_states, "lw d s -> lw s d") # shift context states corresponding to windows context_states = jnp.roll(context_states, 1, axis=1) # replace the initial window with trainable weights init_context = get_init_context(num_states) # (d, s) context_states[0] = init_context # lift to multiple heads context_states = dense(context_states) return context_states # (l/w, s, d, h)
|
2306.09539#79
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09539
| 80 |
Pseudocode 4: Context state collection for BST-MF variants.
"""Block-State Transformer Layer.""" # Block Transformers are non-recurrent and parallelizable. block_transformer = jax.vmap(BRecT.nonrecurrent_cell) def BST(u): """Block-State Transformer Layer."""
18
global MF # True if Multi-Filter, False otherwise (SH/MH) # split inputs into windows (l/w, w, d) u = jnp.split(u, seq_length // win_length, axis=0) # collect context states from SSM outputs context_states = [SH/MH/MF]_context_states(u) # pass the contexts in place of recurrent states y = block_transformer( token_embeddings=u, recurrent_state=context_states, use_cross_attn_causal_mask=not MF, use_cross_positional_emb=MF, # context IDs ) return rearrange(y, "lw w d -> (lw w) d") # (l, d)
Pseudocode 5: Block-State Transformer Layer.
19
|
2306.09539#80
|
Block-State Transformers
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
http://arxiv.org/pdf/2306.09539
|
Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin
|
cs.CL, cs.LG
|
NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems
| null |
cs.CL
|
20230615
|
20231030
|
[
{
"id": "1901.02860"
}
] |
2306.09212
| 81 |
Crop physiology, agroecology, soil science, breeding, ... Gross anatomy, neuroanatomy, clinical anatomy, ... Classical Chinese, poems, words, songs,... Drama, poetry, ink painting, literature, movie, ... Astronautics, planets, galaxies, asteroids, constellations, ... Fairness and justice, transparency and accountability, ... Ancient history, modern history, ancient culture, ... Poetry, prose, drama, literary theory, ... Science, law, Confucian classics, logic, common sense, ... Emergency procedures, signs, signals, traffic laws, ... Regional cuisines, cultural significance, nutrition, ... Chinaâs foreign policyâs principles, goals, history, ... Educational theory, pedagogy, psychology, language, ... Anatomy, physiology, healthcare, diagnose, pathology, ... Factor reduction tables, density functions, ... Modern education, ancient education, school education, ... Air pressure, altitude, precipitation, ... Criminal patterns, patent law, marriage law, ... Matrices, derivatives, random variables, ... Probability, statistical tests, linear regression Biochemistry, organic chemistry, genetics,
|
2306.09212#81
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 81 |
Table 8: RQ2 â Comparison of different Intervention Functions with a smaller teacher (Flan-T5- Large) and a larger student (Flan-T5-XL) on StrategyQA. The teacher assumes access to gold labels.
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random EU (w/ teacher answers) EU (w/ gold label) 61.13±2.72 61.13±2.72 61.13±2.72 62.29±1.53 66.22±2.63 66.52±3.27 64.91±0.67 67.39±2.40 70.16±0.90 66.08±1.76 69.28±1.76 71.47±1.09 68.99±3.14 70.59±3.81 72.78±2.48 69.43±3.41 69.43±3.41 69.43±3.41
|
2306.09299#81
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 82 |
law, marriage law, ... Matrices, derivatives, random variables, ... Probability, statistical tests, linear regression Biochemistry, organic chemistry, genetics, metabolism, ... Data structures, algorithms, programming, operating systems, ... Network security, cryptography, firewalls, network protocols, ... Mechanics, waves, power, energy, light, electricity, ... Planning, contracts, safety, budgeting, management, ... Microeconomics, macroeconomics, economic systems, policy, ... Educational psychology, policies, technology, management ... Electromagnetics, Ohmâs Law, power Systems, ... Ancient poems, classics, pronunciation, meaning, ... heatstroke, fire, diet, first aid, ... windows, word, powerpoint, ... Trigonometry, plane geometry, solid geometry, arithmetic, ... Minority cultures, policies, religion, beliefs, history, ... Chemistry, microbiology, processing, preservation, nutrition, ... Mendelian Genetics, chromosomes, DNA, genetic disorders, ... International economics, organizations, global events, ... Cell biology, genetics, evolution, ecology, microbiology, ... Atomic, synthesis, chemical equilibrium, acid-base reactions,
|
2306.09212#82
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 82 |
Table 9: RQ2 â Comparison of Expected Utility (with and without access to gold labels) with random intervention, involving a LLaMA-7B student and a LLaMA-65B teacher. EU = Expected Utility. Importantly, even when the teacher does not have access to the gold labels, expected utility with teacher answers (second row) leads to a statistically significant 5% improvement in student accuracy (p = 0.02) at 20% intervention.
|
2306.09299#82
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 83 |
organizations, global events, ... Cell biology, genetics, evolution, ecology, microbiology, ... Atomic, synthesis, chemical equilibrium, acid-base reactions, ... Physical geography, human geography, environmental geography, ... Equations, trigonometry, analytic geometry, probability, ... Mechanics, heat, optics, electricity, acoustics, nuclear physics, ... Marxist philosophy, political economy, scientific socialism, ... Reproductive health, contraceptive methods, mental health, ... Treaties, agreements, national sovereignty, law of the sea, ... Media effects theory, communication models, journalism law, ... Constitution, Administrative Law, Civil Law, Criminal Law, ... Legal ethics, moral views and values, social ethics, history, ... Propositional logic, inductive reasoning, critical thinking, ... Supervised learning, unsupervised learning, neural networks, ... Organizational theory, leadership, international management, ... Marketing Concepts, Pricing Strategies, Consumer Behavior, ... Basic principles, Practical significance, contemporary value, ... Grammar, semantic, literature, ... Dietary fiber, trace elements, fatty acids, ... Chinese Philosophy, Western Philosophy, Book
|
2306.09212#83
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 83 |
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Expected Student Conf (Pre) â Expected Student Conf (Post) â Expected Utility â True Student Conf (Pre) â True Student Conf (Post) â True Utility â 84.79±0.41 84.79±0.41 84.79±0.41 84.79±0.41 84.79±0.41 84.79±0.41 84.79±0.41 85.79±0.48 84.57±0.69 86.66±0.37 87.34±1.09 92.03±0.19 87.40±0.39 92.87±0.18 86.79±0.84 86.35±0.73 88.69±0.19 89.33±0.55 91.70±0.04 89.59±0.53 93.99±0.02 87.46±0.20 87.99±0.87 90.76±0.06 90.27±0.40 91.03±0.34 92.31±0.09 94.65±0.13 88.52±0.39 89.51±0.82 92.43±0.61 91.30±0.22 90.27±0.41
|
2306.09299#83
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 84 |
... Grammar, semantic, literature, ... Dietary fiber, trace elements, fatty acids, ... Chinese Philosophy, Western Philosophy, Book of Changes, ... Audit, financing, assets, profit distribution, ... Patent Law, Criminal Law, Contract Law, ... Clinical Trials, Fractures, HIV, ... emotions, thought patterns, perception, ... Negotiations, Organizational Image, Etiquette, ... national security, terrorism, ... Socialization, cities and community, ... swimming, Chinese martial arts, heart rate, ... human meridians, yin and yang, ... Pathogen, viral gene mutation, infection Ancient civilizations, the Industrial Revolution, world wars, ... Islam, Judaism, Buddhism, Christianity, ...
|
2306.09212#84
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 85 |
Other STEM Social Science Humanities STEM Social Science Humanities Humanities Social Science Other Social Science Social Science Social Science STEM STEM Social Science STEM Humanities STEM STEM STEM STEM STEM STEM Other Social Science Social Science STEM Social Science Other Other STEM Social Science Other STEM Humanities STEM STEM Social Science STEM STEM Social Science Other Humanities Social Science Humanities Other Humanities STEM Social Science Social Science Humanities Social Science STEM Humanities Social Science Humanities STEM Social Science Social Science Social Science Social Science Other Other STEM Humanities Humanities
16
# # Q
169 148 164 160 165 209 323 204 160 131 136 107 179 237 106 107 106 108 105 106 273 204 171 147 139 159 163 172 252 198 238 230 135 143 176 149 169 132 118 164 110 143 126 185 172 411 214 123 122 210 180 189 116 145 105 175 211 376 232 174 135 226 165 185 169 161 160
Under review
Table 7: The statistics of the CMMLU test set, where Q represents the question and C indicates the answer choices.
|
2306.09212#85
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 85 |
Table 10: RQ2 â Comparison of different Intervention Functions with a Flan-T5-Large student and a Flan-T5-XL teacher on CommonsenseQA. The teacher assumes access to gold labels.
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Expected Student Conf (Pre) â Expected Student Conf (Post) â Expected Utility â 9.62±1.53 9.62±1.53 9.62±1.53 9.62±1.53 10.20±1.06 11.11±1.44 12.80±1.28 13.68±1.87 10.68±0.82 11.37±1.17 12.91±0.58 14.06±1.44 11.24±0.50 11.56±1.34 13.10±0.10 13.99±0.80 11.92±1.15 12.40±1.01 12.72±2.14 13.68±0.58 12.25±0.94 12.25±0.94 12.25±0.94 12.25±0.94
Table 11: RQ2 â Comparison of different Intervention Functions with a LLaMA-7B student and a LLaMA-13B teacher on GSM8k. The teacher assumes access to gold labels.
|
2306.09299#85
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 86 |
Under review
Table 7: The statistics of the CMMLU test set, where Q represents the question and C indicates the answer choices.
Subject Tasks #Q Avg. #Q Max. #Q Min.#Q Avg.Q Tokens Avg.C Tokens STEM Humanities Social Science Other China-specific 17 13 22 15 15 2531 2489 3652 2910 2572 148.88 191.46 166.00 194.00 171.46 230 411 252 376 323 105 105 107 126 107 38.53 41.65 36.84 31.31 44.54 11.62 10.10 7.25 7.02 8.20 All 67 11582 172.87 411 105 36.85 8.76
17
Under review
C CMMLU EXAMPLES Table 8: Examples with their corresponding English translations from CMMLU among different subjects, where the bold items indicate the correct choices.
|
2306.09212#86
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 86 |
compare the accuracy on StrategyQA when the teacher (Flan-T5-XL) does not have access to gold labels.
Results with weaker Flan-T5-Large teacher and stronger Flan-T5-XL student. RQ1 demon- strated that random intervention by a smaller teacher may not benefit a larger student. But, does Expected Utility benefit in such scenarios? We show this through Figure 8, which compares the accuracy on StrategyQA with Flan-T5-Large as the teacher and Flan-T5-XL as the student. While random intervention shows a monotonically decreasing trend with more intervention, Expected Utility improves the accuracy by 2% (68% â 70%) by paying 20% intervention cost and by 4% by paying 60% cost. Thus, we conclude that weaker teachers can also teach stronger students with
19
|
2306.09299#86
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 87 |
Subject STEM Question æ²¹ç½è½¦åé¢é½æä¸æ¡æå°çéé¾ï¼å
¶ä½ ç¨æ¯ï¼ Choices A. ä½ä¸ºæ²¹ç½è½¦çæ å¿ B. åå¤çæ£ç C. ååºå声ï¼æ示å
¶ä»è½¦è¾åè¡äºº D. æçµè·å¯¼å
¥å¤§å°ï¼é¿å
ç±éçµé æ çå±å®³ What is the purpose of the iron chain dragging on the ground behind an oil tanker? é¿ç¯å°è¯´ã京åçäºãçä½è
æ¯ï¼ A. As a symbol of an oil tanker B. Dissipating heat to the outside world C. Emitting sound to alert other vehicles and pedestrians D. Conducting electric charges into the ground to prevent hazards caused by static electricity A. ä¸ç² B. æç³ C. æè¯å D. èè Humanities Who
|
2306.09212#87
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 87 |
19
Intervention Budget Teacher Explanation Type 0% 20% 40% 60% 80% 100% Unpersonalized-Rationales Unpersonalized-CoT Personalized Human Explanations 58.51±2.00 58.51±2.00 58.51±2.00 58.51±2.00 66.52±2.97 67.83±1.53 69.28±1.26 72.34±0.90 69.14±1.76 71.32±1.33 71.61±1.15 77.72±0.75 70.16±1.09 71.17±1.15 72.63±1.33 81.51±1.09 67.97±0.50 69.86±2.43 68.55±1.90 82.09±0.87 60.40±0.50 62.96±2.47 62.73±2.80 81.36±0.66
Table 12: RQ3 â Comparison of different kinds of teacher explanations (unpersonalized-rationales, unpersonalized-CoT, personalized, and human) on the student accuracy for StrategyQA. Here Flan- T5-Large is the student model and Flan-T5-XL is the teacher model.
|
2306.09299#87
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 88 |
caused by static electricity A. ä¸ç² B. æç³ C. æè¯å D. èè Humanities Who is the author of the novel âMoment in Pekingâ? âæé¥âæ¯ï¼ï¼çç¹è²é¥®é£ A. Ding Ling B. Rou Shi C. Lin Yutang D. Lao She A. èæ B. ç»´å¾å°æ C. èæ D. æé²æ Social Science âPilafâ is a characteristic cuisine of () å
¨èº«é»ææ¯é£ç¨ï¼ï¼è¿é A. Zang nationality B. Uygur C. Miao nationality D. Chaoxian nationality A. ç»´çç´ A B. ç»´çç´ D C. ç»´çç´ B D. ç»´çç´ C Other The yellowing of the whole body is a result of excessive consumption of () ååå¼åä¸æ
é¿åçæçæ¯è°ï¼ A.
|
2306.09212#88
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 88 |
Intervention Budget Teacher Explanation Type 0% 20% 40% 60% 80% 100% Unpersonalized-CoT Personalized 61.13±2.72 61.13±2.72 66.52±1.27 68.95±1.26 70.16±0.90 71.86±2.72 71.47±1.09 72.61±1.96 72.78±2.48 73.17±4.00 69.43±3.41 69.57±1.53
Table 13: RQ3 â Comparison of unpersonalized and personalized teacher explanations on the student accuracy for StrategyQA. Here LLaMA-7B is the student model and LLaMA-65B is the teacher model.
#Rounds Demonstrations Type 1 2 3 4 5 No Explanations Student Explanations Teacher Explanations 55.45±2.26 56.08±4.16 55.74±2.40 56.04±4.19 55.31±3.14 60.84±3.71 58.95±4.16 54.24±2.00 59.97±2.66 57.35±3.21 53.90±4.21 59.82±4.55 57.93±2.66 53.85±3.73 61.57±1.31
|
2306.09299#88
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09299
| 89 |
Table 14: RQ4 â Results of Multi-turn interaction between the student and the teacher comparing stu- dent accuracy on unexplained test points with unexplained, student-explained and teacher-explained demonstrations.
appropriately designed Intervention Functions, especially when the student and the teacher have some complementary benefits.
Results with LLaMA models. Table 9 compares Expected Utility-based intervention with random intervention for LLaMA models (LLaMA-7B as the student and LLaMA-65B as the teacher) on StrategyQA. We evaluate expected utility in two scenarios â with and without gold labels. Both provide improvements over random intervention, as also observed with the Flan models. In particular, when the teacher does not have access to the gold labels (second row), one can compute expected utility with respect to the teacher predictions and obtain a significant 5% improvement (p = 0.02) in student accuracy at 20% intervention.
Results on Other Datasets. Table 10 compares different Intervention Functions on the Common- senseQA dataset with Flan-T5-Large as the student and Flan-T5-XL as the teacher. Table 11 reports results on the GSM8k dataset with LLaMA-7B as the student and LLaMA-13B as the teacher.
# F RQ3: Additional Results
|
2306.09299#89
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 90 |
Table 8 provides examples from CMMLU in each category.
18
Under review
# D CMMLU DIFFICULTY DISTRIBUTION
We analyze the difficulty distribution of CMMLU from two perspectives. Firstly, the CMMLU benchmark encompasses a diverse range of difficulty levels: 5 subjects at primary school level, 10 at middle/high school level, 23 at college level, and 29 at professional level, ensuring a comprehensive difficulty spectrum.
Secondly, to estimate the difficulty distribution within each subject, we evaluated the top 20 models from our main results table. Each question was treated as a data point, and we recorded the number of models correctly answering each question. This approach allowed us to map out the difficulty distribution across subjects.
|
2306.09212#90
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 90 |
# F RQ3: Additional Results
Table 12 compares different kinds teacher explanations on student accuracy for StrategyQA with Flan-T5-Large as the student model and Flan-T5-XL as the teacher model. Table 13 compares unpersonalized and personalized explanations on StrategyQA with LLaMA-7B as the student model and LLaMA-65B as the teacher model. Figure 12 shows five qualitative examples from StrategyQA of unpersonalized and personalized explanations generated by a LLaMA-65B teacher model for a LLaMA-7B student model. We observe a common pattern that the personalized explanations are shorter, simpler, and more directed toward answering the question. The unpersonalized explanations, while still factually correct, are elaborate (e.g., see âExample 5â) that may end up distracting the
20
Intervention Budget Intervention Function 0% 20% 40% 60% 80% 100% Random Neg Expected Utility 58.51±2.00 58.51±2.00 60.40±1.76 52.98±1.76 61.13±2.65 51.09±1.57 60.98±1.09 50.80±1.40 64.33±4.54 53.42±3.10 62.96±2.47 62.45±1.57
|
2306.09299#90
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 91 |
distribution across subjects. agronomy anatomy ancient_chinese arts astronomy business ethics | | | ! ES cr rr 8. ES ES chinese civil service_exam cchinese_dtiving rule chinese food culture chinese foreign_policy chinese history chinese titerature | | | ES 8. ES ES âchinese teacher_qualifcation clinical knowledge college actuarial science college_education college engineering hydrology college lave | | ES 8. ES ES college mathematics college medical statistics college_medicine computer science computer_security âconceptual_physics | ! 8. ES 2 8 ES âeconomies education electrical engineering elementary. chinese âelementary commonsense : 3 3 § i | | | ES 8. ES ES ementary_information_and technology elementary mathematics ethnology food_sclence genetics global facts | | ES 8. ES ES high_school_biology high_school chemistry high school geography high school_mathematies high _school_physies high school polities ES 8. ES ES human _sexuality International_law Journalism Jurisprudence legal_and_moral_basis logical | | 8. te 2. 8. 8. machine learning management marketing marist. theory modern chinese rutrition | | | | | ES 8. ES
|
2306.09212#91
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 91 |
Table 15: RQ5 â Comparison of random intervention function with negative expected utility, demonstrating that the teacher can hurt the student by intervening on samples where the utility is the lowest.
Intervention Budget Teacher Explanation Type 0% 20% 40% 60% 80% 100% Unpersonalized-CoT Deceiving Explanations 58.51±2.00 58.51±2.00 67.83±1.53 66.95±1.76 71.32±1.33 69.86±1.15 71.17±1.15 68.70±0.66 69.86±2.43 66.66±1.40 62.96±2.47 62.73±3.27
Table 16: RQ5 â Comparison of a deceiving teacher with an unpersonalized teacher on StrategyQA with Flan-T5-Large as the student model and Flan-T5-XL as the teacher model.
student. Hence, the personalized explanations are probably easier to reason over for a comparatively weaker student, LLaMA-7B, leading to better performance.
# G RQ4: Additional Results
Table 14 shows RQ4 results on StrategyQA with LLaMA-7B as the student and LLaMA-65B as the teacher.
# H RQ5: Additional Details and Results
|
2306.09299#91
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 93 |
# ES
Figure 8: Difficulty distribution estimation of each subject. We use violin plot for visualization, where the x-axis represents the number of models that correctly answer a question, and the y-axis indicates the quantity of such questions. A peak on the left side of the plot (e.g., college actuarial science at position [3, 3]) suggests that the subject is generally challenging, as most questions are correctly answered by only a few models. Conversely, a peak on the right (e.g., arts at position [1, 4]) indicates a relatively simpler subject, where most questions are correctly answered by many models. Subjects exhibiting multi-peak distributions reveal a varied difficulty range within that subset. For instance, a hypothetical scenario with a dataset comprising basic arithmetic problems and complex calculus questions would result in a distribution with two distinct peaks separated by a notable gap, resembling a horizontal funnel. This indicates a wide spectrum of difficulty levels, from very easy to highly challenging. Figure 8 reveals that the majority of subjects exhibit a single peak in their difficulty distribution. This single-peak pattern indicates a uniform level of difficulty within these subjects, suggesting a consistent challenge for models across the range of questions. However, certain subjects, such as machine learning (located at position [9, 1]) and professional law (at position [10, 3]), display dual
19
# Under review
|
2306.09212#93
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 93 |
Study Design. This RQ explores the negative implications of both RQ2 (i.e., when to intervene) and RQ3 (i.e., how to generate teacher explanations), now with the goal of deceiving the student. First, extending our Expected Utility-based Intervention Function (RQ2), we rank samples in increasing order of expected utility, such that the teacher intervenes when the utility is the lowest. Here, the teacherâs goal is to communicate explanations for data points where the student gives an incorrect answer by following the teacherâs explanation but would have answered correctly had it leveraged its own explanation. We compare this with random teacher intervention (which is generally helpful). Next, in order to explore the negative implication of RQ3, we make the teacher condition on incorrect answers and non-factual human explanations that we manually generate by perturbing (correct) human explanations. We manually make minimal edits to the explanations such that the CoT reasoning is plausible yet non-factual (see some examples in Fig. 11). Now, the teacherâs goal is to learn from non-factual explanations and generate similar explanations that purposefully mislead the student. We compare this misaligned teacher with an unpersonalized teacher that learns from factual gold explanations (i.e., the baseline from RQ3).
|
2306.09299#93
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 94 |
19
# Under review
peaks. This dual-peak pattern signifies a notable presence of both relatively easy and challenging questions, with fewer intermediate-level questions. Despite the presence of two peaks, the transition between these peaks is gradual rather than abrupt, indicating a smooth progression in difficulty levels within these subjects.
# E EMERGENT ABILITY SHOWN IN CMMLU SUBJECTS
es - je â pe fe is in Ce Be i ie i Ls a ze ie fe : 4 eel ee Ar cl Md as elder fz al et |e de Fs ie fn fae dso Tso Eso he 0 0 i te ye Bs fs i paeede STP Jp i * * â â " Be we - i -e i Lt a x ke se eT TE EL ey BL 4 i G . a ie ic re Be <a Sao 48 ue ke fe be be feo Bao el ee ll de ea a pa ie er odat dy det ep fp fap Jip det | Pr PE} #50 oso S50 Bso dso 250 fs 2 SDA eAL RYE EEE me rer er te er etoja Jet fer [ap der Je ELA YE EL R/ |e
|
2306.09212#94
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 94 |
Main Results. We show the results on StrategyQA in Table 15 and Table 16 with Flan-T5-Large as the student and Flan-T5-XL as the teacher. Ranking data points by negative expected utility allows the teacher to reduce student accuracy to 53% at 20% intervention and as low as random chance at 60% intervention (which is 8 points worse than the student performance with no intervention). Next, Fig. 8(b) illustrates that the teacher can condition on non-factual explanations to successfully generate worse explanations that reduce the student accuracy by at least 2 points, relative to the improvement of benign explanations. Interestingly, many of the explanations are still helpful on an absolute scale, though more than half of the deceiving explanations flip the student prediction to be incorrect (based
21
on the trend declining earlier than the standard CoT explanations). Therefore, our conclusion is that teacher LLM explanations can be influential for student LLMs in both good and bad ways.
# Example 1
Q: Would someone with back pain enjoy picking strawberries? Correct Explanation: Back pain may be worsened by repeated bending at the waist. Strawberries grow very close to the ground. Correct Answer: no Deceiving Explanation: Back pain may be worsened by repeated bending at the waist. Strawberries grow high up on the tree.
Example 2
|
2306.09299#94
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 95 |
Figure 9: LLaMA-2 models performance on each subject. s, m, l means 7B, 13B and 70B models, respectively. We assessed the concept of emergent ability using the LLaMA-2 model family. Figure 9 illustrates the performance of the LLaMA-2 pre-trained models (7B, 13B, and 70B) across various subjects. The figure indicates that, for most subjects, there is a correlation between increased model size and enhanced performance. Notably, in subjects like college education (position [2, 4]), elementary commonsense (position [3, 6]), human sexuality (position [4, 7]), and public relations (position [5, 12]), the performance of the 7B and 13B models is comparable, while the 70B model shows a significant improvement.
However, since LLaMA-2-70B model has been trained on a more extensive dataset compared to its 7B and 13B counterparts, which likely includes more comprehensive coverage in these specific domains. We cannot simply attribute it to emergent ability. In addition, these tasks are mostly belongs to social science rather than STEM (which might need intensive reasoning). Given these complexities, we leave the exploration of emergent ability in our future research endeavors.
20
Under review
# F MODELS BEING EVALUATED
|
2306.09212#95
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 95 |
Example 2
Q: Could a snowy owl survive in the Sonoran? Correct Explanation: The Snowy owl is a bird native to the Arctic regions of North America. Temperatures in the North American Arctic range from -36.4F to 50F. The Sonoran is one of the hottest deserts in the world. The Sonoran Desert can get as hot as 118F. Correct Answer: no Deceiving Explanation: The Snowy owl is a bird native to the Arctic regions of North America. Tem- peratures in the North American Arctic range from -36.4F to 50F. The Sonoran is one of the most snowy mountains in the world.
Example 3
Q: Has Drew Carey outshined Doug Davidsonâs tenure? Correct Explanation: Drew Carey has been the host of the Price is Right for over 13 years. Doug Davidson hosted the Price is Right from 1994-1995. Correct Answer: yes Deceiving Explanation: Drew Carey has been the host of the Price is Right for over 13 years. Doug Davidson is hosting the Price is Right from 1994 until now.
Figure 11: Examples of manually constructed deceiving explanations for StrategyQA that support the wrong answer. The deceiving part of the explanations is highlighted in red.
22
|
2306.09299#95
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 96 |
20
Under review
# F MODELS BEING EVALUATED
ChatGPT/GPT4 are GPT models developed by OpenAI and fine-tuned using reinforcement learning from human feedback (RLHF). As commercial products, specific details about the model size, training data, and training process remain undisclosed.
Falcon is a decoder-only model created by TII and trained on 1,000B tokens of RefinedWeb (Penedo et al., 2023) data. Due to the high quality of its training data, Falcon-40B performs competitively with LLaMA-65B on various benchmarks.
LLaMA is an auto-regressive language model proposed by Meta. It incorporates several structural improvements over the vanilla transformer and is trained on a mixture of publicly available data sources. LLaMA has demonstrated performance that is comparable to or even superior to models that are ten times its size.
LLaMA2 is an upgraded version of LLaMA developed by Meta. The preprocessing stage involves more robust data cleaning and updating data mixes, and the model employs a 40% increase in the total token count during training. Additionally, it up-samples the most factual sources to enhance knowledge and reduce hallucinations. Grouped-query attention (GQA) has been employed to reduce GPU memory usage.
|
2306.09212#96
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 96 |
Example 1 Question: Can a computer be programmed entirely in Boolean algebra? Gold Answer: yes Unpersonalized Teacher Explanation: Boolean algebra is a branch of mathematics that deals with the values true and false. A computer program is a set of instructions that a computer can execute. Student Prediction (with Unpersonalized Explanation): no Personalized Teacher Explanation: Boolean algebra is a branch of mathematics. It is used in computer programming. Student Prediction (with Personalized Explanation): yes Example 2 Question: Would Amy Winehouseâs death have been prevented with Narcan? Gold Answer: no Unpersonalized Teacher Explanation: Amy Winehouse was a British singer who died of alcohol poisoning in 2011. Narcan is a brand name for naloxone, a drug used to treat opioid overdoses. Student Prediction (with Unpersonalized Explanation): yes Personalized Teacher Explanation: Narcan is a drug used to reverse opioid overdoses. Amy Winehouse died of alcohol poisoning. Student Prediction (with Personalized Explanation): no Example 3 Question: Could Brooke Shields succeed at University of Pennsylvania? Gold Answer: yes Unpersonalized Teacher Explanation: Brooke Shields is
|
2306.09299#96
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 97 |
BLOOM is a multi-lingual targeted LLM developed by BigScience. It is trained on 46 natural languages and 13 programming languages. The largest BLOOM model consists of 176B parameters, but deploying such a large model can be challenging. In this paper, we evaluate the performance of the 7B BLOOM model.
BLOOMZ is derived from BLOOM through fine-tuning on a cross-lingual task mixture (xP3), which is an instruction-following dataset. BLOOMZ exhibits competitive performance with models that have a larger number of parameters across various non-generation tasks.
Bactrian-X is a series of LLMs (LLaMA, BLOOM, mT5) proposed by MBZUAI. These models are fine-tuned on a multilingual instruction-following dataset that encompasses 52 languages. All the fine- tuned Bactrian-X models demonstrate performance improvements compared to their corresponding base models in multilingual generation settings.
|
2306.09212#97
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 97 |
no Example 3 Question: Could Brooke Shields succeed at University of Pennsylvania? Gold Answer: yes Unpersonalized Teacher Explanation: Brooke Shields is an American actress. The University of Pennsyl- vania is a private Ivy League university in Philadelphia. Student Prediction (with Unpersonalized Explanation): no Personalized Teacher Explanation: Brooke Shields attended Princeton University. Student Prediction (with Personalized Explanation): yes Example 4 Question: Can Planned Parenthood tell your University that you have Herpes simplex virus? Gold Answer: no Unpersonalized Teacher Explanation: Planned Parenthood is a non-profit organization that provides reproductive health care. Herpes simplex virus is a virus that causes herpes. Herpes is a sexually transmitted disease. Student Prediction (with Unpersonalized Explanation): yes Personalized Teacher Explanation: Planned Parenthood is a non-profit organization that provides repro- ductive health care. They do not have access to your medical records. Student Prediction (with Personalized Explanation): no Example 5
|
2306.09299#97
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 98 |
ChatGLM and ChatGLM2 are bidirectional dense models pre-trained using the General Language Model (GLM) algorithm developed by Tsinghua University. They support bilingual (Chinese and English) language processing. ChatGLM is a version of GLM that is enhanced with supervised fine- tuning, feedback bootstrap, and reinforcement learning with human feedback, specifically optimized for Chinese question answering (QA) and dialogue tasks. In this paper, we evaluate the performance of 10B and 6B models of GLM.
BatGPT jointly developed by Wuhan University and Shanghai Jiaotong University, is a bilingual (Chinese and English) and bidirectional language model. BatGPT is initialized with a novel parameter expansion method, which enables it to absorb knowledge from the pre-training of other LLMs. With a bidirectional autoregressive architecture and further enhancement through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human and AI Feedback (RLHAF), BatGPT is able to handle long-range, multi-turn question-answering tasks effectively and alleviate concerns regarding memory limitations. The evaluation of the 15B version is presented in this work.
|
2306.09212#98
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09299
| 98 |
Question: Did US President during Spanish-American War suffer similar demise to Abraham Lincoln? Gold Answer: yes Unpersonalized Teacher Explanation: The Spanish-American War was fought in 1898. William McKinley was the US President during the Spanish-American War. McKinley was assassinated in 1901. Abraham Lincoln was the US President during the American Civil War. Lincoln was assassinated in 1865. Student Prediction (with Unpersonalized Explanation): no Personalized Teacher Explanation: William McKinley was assassinated in 1901. He was the 25th President of the United States. Student Prediction (with Personalized Explanation): yes
Figure 12: Qualitative comparison between unpersonalized and personalized explanations generated by a LLaMA-65B teacher model for a LLaMA-7B student model for StrategyQA questions. For all these questions, the personalized explanation leads to the correct student answer but the unpersonal- ized one does not. A common pattern is that the personalized explanations are shorter, simpler, and more directed toward answering the question.
23
|
2306.09299#98
|
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
|
A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address this, we consider a student-teacher framework between two
LLM agents and study if, when, and how the teacher should intervene with
natural language explanations to improve the student's performance. Since
communication is expensive, we define a budget such that the teacher only
communicates explanations for a fraction of the data, after which the student
should perform well on its own. We decompose the teaching problem along four
axes: (1) if teacher's test time intervention improve student predictions, (2)
when it is worth explaining a data point, (3) how the teacher should
personalize explanations to better teach the student, and (4) if teacher
explanations also improve students on future unexplained data. We first show
that teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, inspired by the Theory of Mind abilities of effective
teachers, we propose building two few-shot mental models of the student. The
first model defines an Intervention Function that simulates the utility of an
intervention, allowing the teacher to intervene when this utility is the
highest and improving student performance at lower budgets. The second model
enables the teacher to personalize explanations for a particular student and
outperform unpersonalized teachers. We also demonstrate that in multi-turn
interactions, teacher explanations generalize and learning from explained data
improves student performance on future unexplained data. Finally, we verify
that misaligned teachers can lower student performance to random chance by
intentionally misleading them.
|
http://arxiv.org/pdf/2306.09299
|
Swarnadeep Saha, Peter Hase, Mohit Bansal
|
cs.CL, cs.AI, cs.LG
|
NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention
| null |
cs.CL
|
20230615
|
20231114
|
[
{
"id": "2302.13971"
},
{
"id": "2007.12248"
},
{
"id": "2204.02311"
},
{
"id": "2302.08399"
},
{
"id": "2304.05489"
},
{
"id": "2304.11490"
},
{
"id": "2210.11416"
},
{
"id": "2110.14168"
},
{
"id": "2212.10071"
},
{
"id": "1702.08608"
},
{
"id": "2302.02083"
},
{
"id": "2301.12726"
},
{
"id": "2112.04359"
},
{
"id": "1503.02531"
},
{
"id": "2010.04119"
},
{
"id": "2303.12712"
},
{
"id": "2212.08410"
},
{
"id": "2303.17651"
},
{
"id": "2212.09721"
},
{
"id": "2305.11426"
},
{
"id": "2305.14763"
}
] |
2306.09212
| 99 |
MOSS-SFT is an open-source Chinese language model proposed by Fudan University. It is comparable to ChatGPT in terms of training scale and alignment techniques. MOSS-SFT is initialized with CodeGen and further pre-trained on 100B Chinese tokens and 20B English tokens. The Supervised Fine-Tuned (SFT) version of MOSS-SFT enables the model to follow instructions in multi-turn dialogues.
21
Under review
Chinese-LLaMA is part of the Chinese-LLaMA-Alpaca project, an open-source initiative that extends the vocabulary of LLaMA and Alpaca to include more Chinese tokens. The models are then further trained on a larger Chinese corpus to enhance their performance.
Baichuan and Baichuan2 are large language model families publicly released by Baichuan Intelligent Technology. Both include versions with 7B and 13B parameters, as well as base and chat variants. Baichuan models are trained on high-quality corpora totaling 1.4 trillion tokens, which surpasses LLaMA-13B by 40%. The models offer support for both Chinese and English languages, and have an extensive context window of 4096. Baichuan2 series is trained on nearly twice the amount of high-quality data, resulting in additional performance enhancements.
|
2306.09212#99
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 100 |
Xverse is a 13B multilingual large language model developed by Shenzhen Yuanxiang Technology. It is trained on 1.4 trillion tokens from diverse sources and supports an extensive 8k context length, efficient tokenization, and advanced training technologies, making it both versatile and efficient.
InternLM is an open-source, lightweight training framework developed collaboratively by Shang- hai AI Laboratory in partnership with researchers from various universities and companies. Its primary objective is to facilitate model pre-training without the need for extensive dependencies. Uti- lizing a unified codebase, it supports both large-scale cluster pre-training on thousands of GPUs and fine-tuning on a single GPU, achieving remarkable performance enhancements. Notably, InternLM achieves nearly 90% acceleration efficiency when training on 1024 GPUs. Based on the InternLM framework, a model family including 7B and 20B versions as well as base and chat variants was released.
# G STRATEGIES FOR ESTIMATING MODEL CHOICES
In this section, we compare three strategies for multiple-choice question evaluation. We introduce the mechanism of each strategy, explain its rationale, and compare their efficiency, strengths, and weaknesses. For convenience, we assume the question is âtextQâ, and the four choices are: âtextAâ, âtextBâ, âtextCâ, âtextDâ.
|
2306.09212#100
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 101 |
Strategy 1 â Next Token Prediction The idea is to input the question along with all candidate choices and prompt the model with a direct answer text, such as âThe answer is: â. We then retrieve the probabilities of the next predicted token and compare these probabilities over the four choice indicator tokens, typically [A, B, C, D]. The token with the highest probability is treated as the modelâs choice.
⢠Example input:
# â Question: textQ
# A. textA B. textB C. textC D. textD Answer:
Efficiency: High
Pro: Most efficient method.
Con: The model may not tend to generate a token from these choice letters.
⢠How to mitigate the cons: Provide few-shot examples with their expected answers.
⢠Works or frameworks use this strategy: MMLU (Hendrycks et al., 2021a), HELM (Liang et al., 2022).
Strategy 2 â Perplexity Comparison After combining question with all candidate choices. We concatenate each candidate answer with the full question and candidates text. These concatenated texts are then input to the model for a forward pass, and we compute the perplexity for each. The sequence with the lowest perplexity is treated as the modelâs choice.
22
# Under review
Example input (4 inputs): â Question: textQ
|
2306.09212#101
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 102 |
22
# Under review
Example input (4 inputs): â Question: textQ
A. textA B. textB C. textC D. textD Answer: A. textA â Question: textQ
A. textA B. textB C. textC D. textD Answer: B. textB â Question: textQ
A. textA B. textB C. textC D. textD Answer: C. textC â Question: textQ
# A. textA B. textB C. textC D. textD Answer: D. textD
Efficiency: Low ⢠Pro: Aligns with the objective of language model optimization as perplexity reflects the true
probability of a model generating the given text.
⢠Con: Low efficiency. Usually take 4x time (for a 4-choice question) compared to Next Token Prediction.
⢠How to mitigate the cons: Efficient implementation that only computes the same prefix once.
⢠Works or frameworks use this strategy: LM-Evaluation-Harness (Gao et al., 2021), Open- Compass.5
Strategy 3 â Free Generation We input the question and candidate choices to the model and prompt it by asking for the correct choices. We allow the model to continue generating text, and then use the auxiliary method to match the patterns and extract the modelâs choices.
⢠Example input:
# â Question: textQ
|
2306.09212#102
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 103 |
⢠Example input:
# â Question: textQ
# A:textA B:textB C:textC D:textD Answer:
Efficiency: Medium/Low ⢠Pro: Allow various prompting, ⢠Con: Need answer extraction via human/model/regular expression. This process can be costly and error-prone. The generation can be very long, resulting in significant time consumption.
⢠How to mitigate the cons: Train a robust answer extraction model, or design robust regular expressions. Use a small temperature when doing generation.
# 5https://github.com/open-compass/opencompass
23
Under review
Table 9: Comparison of different evaluation strategies. We compare next token prediction (i.e. âNextâ), and free generation (âGenâ). We also list the proportion of responses that cannot matched by our regex (% E). Note that our regex is designed based on the observation of ChatGPT and ChatGLM responses.
|
2306.09212#103
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 104 |
Model Next Gen % E 0-shot Baichuan2-13B-Chat BatGPT-15B-sirius ChatGLM-6B ChatGLM2-6B InternLM-Chat-20B Xverse-13B-Chat 59.79 49.81 40.56 51.48 55.06 55.59 58.77 45.26 40.43 49.61 53.52 52.96 0.71 2.35 1.15 1.51 0.01 0.88 5-shot Baichuan2-13B-Chat BatGPT-15B-sirius ChatGLM-6B ChatGLM2-6B InternLM-Chat-20B Xverse-13B-Chat 59.89 47.88 37.17 49.69 54.52 56.12 54.44 40.13 36.83 48.80 51.51 51.64 6.44 4.58 1.65 0.56 0.42 5.55
⢠Works or frameworks use this strategy: OpenCompass, C-Eval (Huang et al., 2023).
|
2306.09212#104
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 105 |
⢠Works or frameworks use this strategy: OpenCompass, C-Eval (Huang et al., 2023).
Table 9 compares models performance using strategy 1 and strategy 3. Since strategy 2 is time- consuming, we didnât conduct results on it. From the table, we find that using next token prediction achieves a higher score than using the free generation strategy for all models, but the gap is less than 3% for most of the models under the zero-shot setting (with the exception of BatGPT which is about 5%). For both zero-shot and five-shot settings, the gap between strategy 1 and 2 is positively correlated to the proportion of the instances that cannot match any choice using regex. Hence, we believe using the next token prediction to force the model to make a choice among the given choices can effectively reflect its knowledge capacity.
# H REGULAR EXPRESSIONS MATCHING ALGORITHMSL
The pseudocode in Algorithm 1 outlines the ExtractChoice function for extracting choices from an LLM output string.
Initially, the function examines whether the first character of the string corresponds to a valid choice and returns that choice if true. To accommodate the complex responses of different LL.M.s, we adopt a four-step matching mechanism.
|
2306.09212#105
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 106 |
First: Identify and extract choices by seeking patterns of some choice statements, such as the term âanswerâ (answer) followed by valid options. Second: Employ a pattern to recursively identify and extract the choices mentioned in the string, iterating until they finally appear. Third: Use weak single matching patterns. Fourth: Check for responses that mention a single choice.
If there is no matching pattern or unique selection, âEâ is returned by default, indicating that no selection was confidently extracted.
24
Under review
Algorithm 1 Algorithm for Extracting Choices from Response Strings
|
2306.09212#106
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 107 |
1: procedure EXTRACTCHOICE(response) 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30: 31: 32: 33: 34: 35: 36: 37: 38: 39: 40: 41: 42: 43: 44: 45: 46: 47: end if 48: return âEâ 49: 50: end procedure response â convert to string(response) choices â [â²Aâ²,â² Bâ²,â² C â²,â² Dâ²] if first character of response â choices then return first character of response end if patterns1 â [ (râçæ¡(é项)?(æ¯|为)ï¼? ?([ABCD])â, 3), (râçæ¡(æ¯|为)é项 ?([ABCD])â, 2), (râæ
?éæ©?ï¼? ?([ABCD])â, 1), (râ([ABCD]) ?é?项(æ¯|为)?æ£ç¡®â,
|
2306.09212#107
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 108 |
?([ABCD])â, 1), (râ([ABCD]) ?é?项(æ¯|为)?æ£ç¡®â, 1), (râæ£ç¡®ç?é项(æ¯|为) ?([ABCD])â, 2), (râçæ¡(åºè¯¥)?(æ¯|为)([ABCD])â, 3), (râé项 ?([ABCD]) ?(æ¯|为)?æ£ç¡®â, 1), (râéæ©çæ¡ ?([ABCD])â, 1), (râçæ¡?ï¼?([ABCD])â, 1), (râ([ABCD])(é?项)?æ¯?符åé¢æâ, 1), (râçæ¡é项ï¼? ?([ABCD])â, 1), (râçæ¡(é项)?åº?该?为(.*?)([ABCD])â, 3), (râtextbf{\(([ABCD])â, 1) ]
|
2306.09212#108
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 109 |
3), (râtextbf{\(([ABCD])â, 1) ] patterns2 â [ (râ([ABCD])(.*?)å½éâ, 1), (râ([ABCD])(.*?)æ£ç¡®â, 1) ] patterns3 â [ (râ[Ëä¸]æ¯ï¼? ?([ABCD])â, 1), (râËé项([ABCD])â, 1) ] for each patterns in [patterns1, patterns2, patterns3] do for each (pattern, idx) in patterns do if pattern is found in response then answer â matched group(idx) if answer â choices then return answer end if end if end for end for pattern4 â râË[ËABCD]*([ABCD])[ËABCD]*$â if pattern4 is matched in response then answer â matched group(1) if answer â choices then return answer end if
|
2306.09212#109
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 110 |
â· Return E as default if no match is found
25
Under review
# I CORRELATION TO OTHER BENCHMARKS
To investigate the correlation between models performance on CMMLU and other benchmarks, we choose 6 popular English LLMs and 5 benchmarks to conducte correlation analysis.
From Figure 10 we find that CMMLU demonstrates a strong correlation with four of these benchmarks, which span areas such as mathematics, commonsense reasoning, and coding. The exception is the PIQA task, where the relevance is somewhat diminished due to most models achieving high scores (>80%) on this task. However, 0.88 still shows strong positive correlation.
CMMLU vs RACE-M CMMLU vs CommonSenseQa CMMLU vs PIQA CMMLU vs GSMBK CMMLU vs Humanéval oo Pearson corr=0.973 Pearson corr=0.970 Pearson corr=0.880 Pearson corr=0.963 Pearson corr=0.948 Lo 2 50 75 100 25 50 75 100 2 50 75 100 2 50 75 100 2 50 75 100 CMWMLU Scores CMLL Scores CMMLU Scores CMMLU Scores CMMLU Scores Uamal-138 Uama2-138 PIQA Scores GSMBK Scores Humanéval Scores RACE Scores CommonSenseQa Scores
|
2306.09212#110
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 111 |
Figure 10: Correlation between the performance on CMMLU and that of other benchmarks. We choose RACE dataset for general language understanding, CommonSenseQA for commonsense reasoning,, PIQA for general reasoning, GSM8K for mathematics, and HumanEval for code ability.
J BREAKDOWN OF MODEL PERFORMANCE
J.1 RESULTS OF ZERO-SHOT
Table 11 displays zero-shot results of the LLMs on CMMLU by 5 sub-categories.
J.2 THE RESULTS OF EACH SUBJECTS
We compared the 0-shot and 5-shot results of selected LLMs that showed higher performance on each subject in Table 10. We further analyze the performance distribution of multiple LLMs across all subjects in Figure 11. It is evident from the figure that LLMs with higher performance exhibit diverse abilities across various tasks, while those with lower performance face challenges in most subjects. Furthermore, the scatter plot distribution indicates comparable performance levels among LLMs across different subjects.
J.3 THE EFFECT OF CHAIN-OF-THOUGHT PROMPT
Table 12 shows the breakdown of the models performance after using chain-of-thought prompt.
MO iii Average Accuracy
# ChatGPT
LLaMA2-70B Falcon-40B, Baichuan2-13B-Chat ChatGLM2-68 InternLM-Chat-20B
# _BatGPT-18B-sirius
|
2306.09212#111
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 112 |
# _BatGPT-18B-sirius
Figure 11: The performance of selected LLMs on CMMLU on each subject. The results for both 0-shot and 5-shot scenarios are depicted.
26
# Under review
Table 10: The results of 0-shot and 5-shot accuracy per subject. The number on the left of 0-shot and the number on the right of 5-shot. The models are LLaMA2-70B, Falcon-40B, Baichuan2-13B-Chat, ChatGLM2-6B, InternLM-Chat-20B, BatGPT-15B-sirius.
Subject GPT4 LLaMA2 Falcon Baichuan2 ChatGLM2 InternLM BatGPT
|
2306.09212#112
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 113 |
Subject GPT4 LLaMA2 Falcon Baichuan2 ChatGLM2 InternLM BatGPT
Ancient Chinese Chinese Civil Service Exam Chinese Driving Rule Chinese Food Culture Chinese Foreign Policy Chinese History Chinese Literature Chinese Teacher Qualification Construction Project Management Elementary Chinese Elementary Commonsense Ethnology High School Politics Modern Chinese Traditional Chinese Medicine Agronomy Clinical Knowledge College Medicine Computer Security Elementary IT Food Science Human Sexuality Legal And Moral Basis Nutrition Professional Medicine Sports Science Business Ethics College Education Economics Education High School Geography Journalism Management Marketing Professional Accounting Professional Psychology Public Relations Security Study Sociology Arts College Law Global Facts International Law Jurisprudence Logical Marxist Theory Philosophy Professional Law World History World Religions Anatomy Astronomy College Actuarial Science College Engineering Hydrology College Mathematics College Medical Statistics Computer Science Conceptual Physics Electrical Engineering Elementary Mathematics Genetics High School Biology High School Chemistry High School Mathematics High School Physics Machine Learning Virology
|
2306.09212#113
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 114 |
37.2 / 40.9 63.7 / 62.5 82.4 / 88.5 65.4 / 65.4 81.3 / 80.4 76.5 / 77.7 49.5 / 47.5 78.2 / 79.3 51.1 / 54.7 53.2 / 58.7 68.2 / 73.7 63.7 / 74.1 67.1 / 65.7 56.0 / 62.1 58.4 / 60.5 66.3 / 67.5 68.8 / 72.2 72.2 / 75.8 87.7 / 85.4 93.7 / 94.5 74.1 / 76.2 72.2 / 69.8 91.1 / 91.1 73.8 / 72.4 66.5 / 67.3 70.9 / 72.1 70.8 / 73.7 79.4 / 83.2 84.9 / 84.9 63.8 / 64.4 78.0 / 75.4 68.0 / 69.2 82.9 / 84.3 81.7 / 81.7 72.6 / 76.6 81.9 / 81.9 63.8 / 67.2 80.0 / 80.7 72.1 / 73.0 74.4 / 77.5 59.3 / 63.0 71.8 / 77.9 61.1 /
|
2306.09212#114
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 116 |
27.4 / 27.4 50.0 / 53.8 66.4 / 70.2 35.3 / 37.5 62.6 / 63.6 61.9 / 61.0 37.7 / 36.3 59.2 / 65.9 41.7 / 41.7 29.4 / 34.9 46.5 / 49.5 42.2 / 46.7 44.1 / 49.0 34.5 / 40.5 38.4 / 42.2 46.2 / 50.9 42.2 / 43.5 39.6 / 44.7 63.7 / 73.7 76.9 / 77.7 53.1 / 56.6 60.3 / 62.7 82.7 / 85.5 49.7 / 56.6 34.8 / 37.2 51.5 / 57.0 56.9 / 62.7 62.6 / 69.2 55.3 / 57.9 51.5 / 53.4 42.4 / 51.7 54.1 / 61.0 56.7 / 64.8 65.6 / 66.1 51.4 / 61.7 50.0 / 62.5 56.9 / 62.1 54.8 / 67.4 59.3 / 64.2 58.8 / 63.1 39.8 / 42.6 49.0 / 58.4 49.7 /
|
2306.09212#116
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 118 |
26.8 / 29.3 33.8 / 30.6 55.0 / 57.3 33.1 / 41.9 48.6 / 42.1 46.1 / 49.2 27.5 / 32.4 45.8 / 59.2 30.2 / 34.5 28.5 / 28.5 35.6 / 45.5 36.3 / 39.3 35.7 / 41.3 28.4 / 30.2 31.9 / 30.8 35.5 / 39.6 36.7 / 38.0 26.7 / 33.0 40.4 / 45.0 54.6 / 63.3 39.2 / 43.4 45.2 / 48.4 67.3 / 73.8 42.1 / 42.8 26.6 / 32.7 43.6 / 43.0 40.2 / 43.5 55.1 / 53.3 48.4 / 49.1 41.7 / 44.2 44.1 / 42.4 43.0 / 45.3 49.5 / 49.5 43.9 / 54.4 41.1 / 50.3 42.2 / 50.9 46.0 / 52.3 48.1 / 48.9 41.2 / 47.8 50.6 / 53.1 31.3 / 35.4 39.5 / 46.7 40.0 /
|
2306.09212#118
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 120 |
40.9 / 37.8 61.9 / 54.4 77.1 / 80.9 60.3 / 64.7 74.8 / 72.0 72.8 / 69.7 57.4 / 57.4 79.3 / 77.7 43.2 / 43.2 57.9 / 61.1 62.6 / 71.2 65.9 / 59.3 76.9 / 67.8 45.7 / 45.7 55.1 / 52.4 58.0 / 61.5 51.5 / 51.1 56.4 / 56.0 66.1 / 68.4 79.0 / 75.6 60.1 / 60.8 61.1 / 61.9 92.1 / 93.0 57.9 / 64.8 50.5 / 50.5 60.0 / 60.0 59.8 / 55.5 72.9 / 76.6 62.3 / 64.2 69.9 / 70.6 66.1 / 67.8 59.3 / 62.2 68.6 / 71.9 67.8 / 63.3 70.3 / 72.0 70.3 / 72.4 64.4 / 55.7 70.4 / 73.3 64.2 / 68.1 83.1 / 83.1 55.6 / 54.6 71.1 / 64.4 56.2 /
|
2306.09212#120
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 122 |
26.8 / 29.9 51.2 / 50.0 60.3 / 62.6 50.0 / 41.9 60.7 / 54.2 61.0 / 69.3 36.3 / 34.8 61.5 / 59.8 36.7 / 38.1 45.6 / 44.8 52.5 / 49.0 48.1 / 42.2 49.0 / 50.3 44.0 / 39.7 48.1 / 53.5 46.7 / 42.6 44.3 / 40.1 42.9 / 45.1 56.1 / 56.1 68.1 / 63.9 49.7 / 43.4 48.4 / 43.7 83.6 / 82.2 53.1 / 47.6 37.5 / 36.7 49.7 / 49.1 46.4 / 42.6 64.5 / 68.2 46.5 / 44.0 60.1 / 60.7 47.5 / 54.2 52.9 / 48.3 62.9 / 61.0 57.2 / 56.7 56.6 / 54.9 55.6 / 58.6 51.1 / 53.4 58.5 / 63.7 51.3 / 47.3 66.2 / 68.1 45.4 / 42.6 57.0 / 49.0 38.4 /
|
2306.09212#122
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
2306.09212
| 124 |
33.5 / 36.0 49.4 / 52.5 67.2 / 68.7 52.2 / 52.9 71.0 / 63.6 77.1 / 78.3 48.0 / 48.5 75.4 / 72.1 44.6 / 48.2 48.0 / 44.4 55.6 / 56.1 63.0 / 55.6 53.8 / 51.7 41.4 / 45.7 48.6 / 46.5 56.2 / 55.0 45.1 / 43.9 40.3 / 45.4 71.3 / 68.4 73.5 / 74.8 55.2 / 49.7 61.1 / 60.3 90.2 / 90.2 52.4 / 54.5 41.0 / 39.6 60.6 / 63.0 56.5 / 59.8 72.9 / 72.9 55.3 / 56.6 60.1 / 61.3 56.8 / 55.1 55.8 / 54.1 65.2 / 67.6 67.2 / 66.7 55.4 / 59.4 68.5 / 68.5 55.2 / 58.0 64.4 / 62.2 58.8 / 59.3 75.6 / 71.9 47.2 / 50.0 64.4 / 61.7 47.6 /
|
2306.09212#124
|
CMMLU: Measuring massive multitask language understanding in Chinese
|
As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, engineering, and humanities. We conduct a thorough evaluation of 18
advanced multilingual- and Chinese-oriented LLMs, assessing their performance
across different subjects and settings. The results reveal that most existing
LLMs struggle to achieve an average accuracy of 50%, even when provided with
in-context examples and chain-of-thought prompts, whereas the random baseline
stands at 25%. This highlights significant room for improvement in LLMs.
Additionally, we conduct extensive experiments to identify factors impacting
the models' performance and propose directions for enhancing LLMs. CMMLU fills
the gap in evaluating the knowledge and reasoning capabilities of large
language models within the Chinese context.
|
http://arxiv.org/pdf/2306.09212
|
Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
|
cs.CL
| null | null |
cs.CL
|
20230615
|
20240117
|
[
{
"id": "2302.13971"
},
{
"id": "2304.12986"
},
{
"id": "2307.00360"
},
{
"id": "2211.09110"
},
{
"id": "2305.08322"
},
{
"id": "2307.15020"
},
{
"id": "2307.09288"
},
{
"id": "2305.15011"
},
{
"id": "2303.08774"
},
{
"id": "2306.01116"
},
{
"id": "2304.08177"
},
{
"id": "2305.10263"
}
] |
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