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Large Language Models for Information Retrieval: A Survey

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2308.07107

Summary

This survey maps how large language models are changing text information retrieval. The authors start from a known tension. Classic term-based methods such as BM25 are fast. Neural models capture meaning better but suffer from scarce training data, weak interpretability and plausible-sounding errors. LLMs like ChatGPT and GPT-4 bring strong understanding, generation and reasoning abilities. The question is where these abilities help a retrieval pipeline.

The paper organizes the literature around four pipeline stages:

  • query rewriting before retrieval,
  • retrieval itself,
  • reranking of a small candidate set,
  • a "reader" that condenses retrieved documents into an answer, as in New Bing.

It then covers LLM-driven search agents that handle several IR tasks at once, and closes with open research directions.

The survey runs no new experiments. Its contribution is a structured taxonomy and an ongoing paper collection, which gives newcomers and practitioners a map of a fast-moving area. This is useful for anyone building search or RAG-style systems, including for lower-resource languages.

Key points

  • Four LLM-enhanced components are reviewed, one per section: query rewriter, retriever, reranker and reader. Search agents get their own section, followed by future directions.
  • The authors use a working definition of "LLM" as a model with more than 1B parameters. Some smaller but representative methods are still included.
  • The historical background runs from the Boolean model and vector space model to statistical language models, BM25 and neural IR.
  • Example backbone models named in the overview figure are ChatGPT, LLaMA, Flan-T5, GLM and BLOOM.
  • The authors maintain a companion paper list, LLM4IR, at github.com/RUC-NLPIR/LLM4IR-Survey, and update the survey periodically (v5, September 2025). The authors are from Renmin University of China and the Beijing Academy of Artificial Intelligence.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

137 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 133 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3748304 · arXiv 2308.07107

@misc{dou2023large,
  title = {{Large Language Models for Information Retrieval: A Survey}},
  author = {Dou;, Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng and Wen, Ji-Rong},
  year = {2023},
  doi = {10.1145/3748304},
  eprint = {2308.07107},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3748304}
}

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