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2308.07107#112 | Large Language Models for Information Retrieval: A Survey | â ¢ Text generation evaluation. The wide application of LLMs in IR has led to a notable enhancement in their generation capability. Consequently, there is an imperative demand for novel evaluation strategies to effectively evaluate the per- formance of passage or answer generation. Previous evalu- ation metrics for text generation have several limitations, including: (1) Dependency on lexical matching: methods such as BLEU [223] or ROUGE [224] primarily evaluate the quality of generated outputs based on n-gram matching. This approach cannot account for lexical diversity and con- textual semantics. As a result, models may favor generating common phrases or sentence structures rather than produc- ing creative and novel content. (2) Insensitivity to subtle differences: existing evaluation methods may be insensitive to subtle differences in generated outputs. For example, if a generated output has minor semantic differences from the reference answer but is otherwise similar, traditional meth- ods might overlook these nuanced distinctions. (3) Lack of ability to evaluate factuality: LLMs are prone to generating â hallucinationâ problems [225â 228]. | 2308.07107#111 | 2308.07107#113 | 2308.07107 | [
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2308.07107#113 | Large Language Models for Information Retrieval: A Survey | The hallucinated texts can closely resemble the oracle texts in terms of vocabulary usage, sentence structures, and patterns, while having non- factual content. Existing methods are hard to identify such problems, while the incorporation of additional knowledge sources such as knowledge bases or reference texts could potentially aid in addressing this challenge. # 8.7 Bias Since ChatGPT was released, LLMs have drawn much at- tention from both academia and industry. The wide appli- cations of LLMs have led to a notable increase in content on the Internet that is not authored by humans but rather generated by these language models. However, as LLMs may hallucinate and generate non-factual texts, the increas- ing number of LLM-generated contents also brings worries that these contents may provide fictitious information for users across IR systems. More severely, researchers [229, 230] show that some modules in IR systems such as retriever and reranker, especially those based on neural models, may pre- fer LLM-generated documents, since their topics are more consistent and the perplexity of them are lower compared with human-written documents. The authors refer to this phenomenon as the â source biasâ towards LLM-generated text. It is challenging but necessary to consider how to build IR systems free from this category of bias. 9 CONCLUSION In this survey, we have conducted a thorough exploration of the transformative impact of LLMs on IR across various dimensions. We have organized existing approaches into distinct categories based on their functions: query rewrit- ing, retrieval, reranking, and reader modules. In the do- main of query rewriting, LLMs have demonstrated their effectiveness in understanding ambiguous or multi-faceted queries, enhancing the accuracy of intent identification. In the context of retrieval, LLMs have improved retrieval accu- racy by enabling more nuanced matching between queries and documents, considering context as well. Within the reranking realm, LLM-enhanced models consider more fine- grained linguistic nuances when re-ordering results. The incorporation of reader modules in IR systems represents a significant step towards generating comprehensive re- sponses instead of mere document lists. The integration of LLMs into IR systems has brought about a fundamental change in how users engage with information and knowl- edge. From query rewriting to retrieval, reranking, and | 2308.07107#112 | 2308.07107#114 | 2308.07107 | [
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2308.07107#114 | Large Language Models for Information Retrieval: A Survey | 21 reader modules, LLMs have enriched each aspect of the IR process with advanced linguistic comprehension, semantic representation, and context-sensitive handling. As this field continues to progress, the journey of LLMs in IR portends a future characterized by more personalized, precise, and user-centric search encounters. This survey focuses on reviewing recent studies of ap- plying LLMs to different IR components and using LLMs as search agents. Beyond this, a more significant problem brought by the appearance of LLMs is: is the conventional IR framework necessary in the era of LLMs? For example, traditional IR aims to return a ranking list of documents that are relevant to issued queries. However, the devel- opment of generative language models has introduced a novel paradigm: the direct generation of answers to input questions. Furthermore, according to a recent perspective paper [53], IR might evolve into a fundamental service for diverse systems. For example, in a multi-agent simulation system [231], an IR component can be used for memory recall. This implies that there will be many new challenges in future IR. REFERENCES [1] Y. Wu, W. Wu, C. Xing, M. Zhou, and Z. Li, â | 2308.07107#113 | 2308.07107#115 | 2308.07107 | [
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