mlsanityData to 7 Oct 2026

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

arXiv 2405.06211 ↗First posted 10 May 2024cs.CL cs.AI cs.IRcited by 450

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations, such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: architectures, training strategies, and applications. As the preliminary knowledge, we briefly introduce the foundations and recent advances of LLMs. Then, to illustrate the practical significance of RAG for LLMs, we systematically review mainstream relevant work by their architectures, training strategies, and application areas, detailing specifically the challenges of each and the corresponding capabilities of RA-LLMs. Finally, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/

Key facts. Cited by 450 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 125 of its 184 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

125 of 184 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation6 May
  2. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  4. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory3 May
  5. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation28 Sep

Other references · 120

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  7. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  8. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  10. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  11. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  12. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
38 more
  1. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  2. 2019Language Models as Knowledge Bases?cited by 1,922
  3. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  4. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
  5. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  6. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  7. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
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  9. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 975
  10. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  11. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  12. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  13. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  14. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  15. 2019Graph Neural Networks for Social Recommendationcited by 711
  16. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  17. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  18. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  19. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  20. 2023Active Retrieval Augmented Generationcited by 506
  21. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 385
  22. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  23. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 347
  24. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  25. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  26. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
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  28. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 282
  29. 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 266
  30. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 242
  31. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  32. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  33. 2021Internet-Augmented Dialogue Generationcited by 217
  34. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  35. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  36. 2020How Context Affects Language Models' Factual Predictionscited by 181
  37. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  38. 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 58; 2025: 237; 2026: 155.

450 papers.

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  1. 2025R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learningcited by 322
  2. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  3. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  4. 2024Agent S: An Open Agentic Framework that Uses Computers Like a Humancited by 182
  5. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  6. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  7. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 122
  8. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  9. 2024MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Enginescited by 98
  10. 2025A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Modelscited by 87
  11. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilotcited by 83
  12. 2025StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimizationcited by 82
38 more
  1. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 69
  2. 2025Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learningcited by 68
  3. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  4. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 65
  5. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  6. 2025Retrieval-Augmented Generation with Hierarchical Knowledgecited by 46
  7. 2025Test-Time Learning for Large Language Modelscited by 46
  8. 2025In-depth Analysis of Graph-based RAG in a Unified Frameworkcited by 45
  9. 2024A Survey of Mambacited by 39
  10. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 38
  11. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 36
  12. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  13. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 34
  14. 2024Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refusecited by 33
  15. 2025Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoningcited by 33
  16. 2025ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewardingcited by 32
  17. 2025Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systemscited by 32
  18. 2025Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlookcited by 32
  19. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 29
  20. 2025KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAGcited by 29
  21. 2025SGMem: Sentence Graph Memory for Long-Term Conversational Agentscited by 29
  22. 2024GalleryGPT: Analyzing Paintings with Large Multimodal Modelscited by 28
  23. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generationcited by 28
  24. 2024P/D-Serve: Serving Disaggregated Large Language Model at Scalecited by 27
  25. 2025ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generationcited by 27
  26. 2025SurveyX: Academic Survey Automation via Large Language Modelscited by 27
  27. 2025SciSciGPT: Advancing Human-AI Collaboration in the Science of Sciencecited by 26
  28. 2025A Survey of Personalization: From RAG to Agentcited by 26
  29. 2024Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generationcited by 25
  30. 2025Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generationcited by 23
  31. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  32. 2025Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learningcited by 22
  33. 2025A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applicationscited by 21
  34. 2024KGARevion: An AI Agent for Knowledge-Intensive Biomedical QAcited by 20
  35. 2024When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?cited by 20
  36. 2024Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generatorcited by 20
  37. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  38. 2025AI Scientists Fail Without Strong Implementation Capabilitycited by 20

50 most cited of 450 All 450 (JSON)

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  5. 2024The Power of Noise: Redefining Retrieval for RAG Systems
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