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A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

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/2405.06211

Summary

Large language models produce fluent text but still invent facts, lack specialised domain knowledge, and fall out of date. Retraining them on fresh or domain-specific data is expensive. This survey examines how retrieval-augmented generation (RAG) addresses these weaknesses. In RAG, a model pulls relevant documents from an external store and conditions its output on them, often with little or no extra training. The authors first recap LLM basics: the encoder-only, decoder-only and encoder-decoder families, plus prompt learning. They then organise the literature on Retrieval-Augmented Large Language Models (RA-LLMs) along three axes:

  • Architecture: how retrieval, generation and augmentation are designed, and whether and how often to retrieve.
  • Training strategies.
  • Application areas.

They close with current limitations and future research directions. The paper offers no new model or dataset. Its value is a structured map of a fast-moving field. It differs from parallel surveys by grouping systems according to technical design and training paradigm rather than modality or broad AIGC scope.

Key points

  • This is the extended version of a survey accepted at KDD 2024. An accompanying website (advanced-recommender-systems.github.io/RAG-Meets-LLMs/) tracks updates.
  • The motivating evidence includes a cited study in which state-of-the-art LLMs hallucinated in 69% to 88% of answers to specific legal queries.
  • The background section uses BERT, GPT and T5 as examples of the encoder-only, decoder-only and encoder-decoder LLM types.
  • The survey places representative methods in four groups, positioned by publication time and citation impact:
    • RAG frameworks/pipelines, e.g., REALM, RAG, FiD, RETRO, FLARE, Self-RAG.
    • RAG learning, e.g., Atlas, EMDR2, RA-DIT.
    • Retriever learning, e.g., DPR, Contriever, UPRISE.
    • Pre-/post-retrieval techniques, e.g., Query2doc, RECOMP, BlendFilter.
  • The covered applications include open-domain QA, dialogue, recommendation, molecule discovery, scientific QA and software engineering.

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

210 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 207 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2405.06211 · arXiv 2405.06211

@misc{chua2024survey,
  title = {{A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models}},
  author = {Chua;, Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng and Li, Qing},
  year = {2024},
  doi = {10.48550/arxiv.2405.06211},
  eprint = {2405.06211},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2405.06211}
}

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