mlsanityData to 14 Oct 2026

Retrieval-Augmented Generation for Large Language Models: A Survey

arXiv 2312.10997 ↗First posted 18 Dec 2023cs.CL cs.AIcited by 2,212

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 1 more
Abstract (arXiv)

Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques. The paper highlights the state-of-the-art technologies embedded in each of these critical components, providing a profound understanding of the advancements in RAG systems. Furthermore, this paper introduces up-to-date evaluation framework and benchmark. At the end, this article delineates the challenges currently faced and points out prospective avenues for research and development.

Key facts. Cited by 2,212 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 174 of its 227 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

174 of 227 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions30 Nov
  2. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning2 Oct
  3. 2023Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT10 Apr
  4. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions20 Dec

Other references · 170

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Citing papers per year: 2023: 6; 2024: 693; 2025: 941; 2026: 572.

2,212 papers.

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  26. 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Modelscited by 150
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  28. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
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