mlsanityData to 7 Oct 2026

A Survey on Retrieval-Augmented Text Generation for Large Language Models

arXiv 2404.10981 ↗First posted 17 Apr 2024cs.IR cs.AI cs.CLcited by 77

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation, offering a detailed perspective from the retrieval viewpoint. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies. Additionally, the paper introduces evaluation methods for RAG, addressing the challenges faced and proposing future research directions. By offering an organized framework and categorization, the study aims to consolidate existing research on RAG, clarify its technological underpinnings, and highlight its potential to broaden the adaptability and applications of LLMs.

Key facts. Cited by 77 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 121 of its 200 references that are in the map. First posted on arXiv 17 Apr 2024.

Builds on

121 of 200 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation9 Apr
  2. 2024PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design8 Mar
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  4. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  6. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov
  7. 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models15 Oct
  8. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory3 May
  9. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation28 Sep
  10. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 111

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2022Training language models to follow instructions with human feedbackcited by 15,712
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  7. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  8. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  9. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  10. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  11. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  12. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
38 more
  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  7. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  8. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  9. 2017Billion-scale similarity search with GPUscited by 2,477
  10. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  11. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  12. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  13. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  14. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  15. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  16. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  17. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  18. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
  19. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  20. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  21. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  22. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  23. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  24. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  25. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  26. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  27. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  28. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  29. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 853
  30. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  31. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
  32. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  33. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
  34. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  35. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  36. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 556
  37. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  38. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519

50 most cited of 111 All 111 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 24; 2025: 31; 2026: 22.

77 papers.

Sort:

By area:

  1. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  2. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systemscited by 115
  3. 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communitiescited by 84
  4. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  5. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generationcited by 59
  6. 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agentscited by 47
  7. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworkscited by 41
  8. 2026A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agentscited by 40
  9. 2025KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Modelcited by 35
  10. 2024Beyond Prompts: Dynamic Conversational Benchmarking of Large Language Modelscited by 20
  11. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  12. 2024Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answeringcited by 19
38 more
  1. 2024Linq-Embed-Mistral Technical Reportcited by 18
  2. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  3. 2024Mixture of In-Context Experts Enhance LLMs' Long Context Awarenesscited by 17
  4. 2025Memory-enhanced Retrieval Augmentation for Long Video Understandingcited by 17
  5. 2024Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequencecited by 15
  6. 2026ProAgentBench: Evaluating LLM Agents for Proactive Assistance with Real-World Datacited by 15
  7. 2025MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystackscited by 11
  8. 2024A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoningcited by 8
  9. 2024Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigationcited by 7
  10. 2025Multi-Agent LLM Judge: automatic personalized LLM judge design for evaluating natural language generation applicationscited by 7
  11. 2025AURA: A Fine-Grained Benchmark and Decomposed Metric for Audio-Visual Reasoningcited by 7
  12. 2025NeuroGenPoisoning: Neuron-Guided Attacks on Retrieval-Augmented Generation of LLM via Genetic Optimization of External Knowledgecited by 7
  13. 2025Affordable AI Assistants with Knowledge Graph of Thoughtscited by 6
  14. 2025Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaborationcited by 5
  15. 2025Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agentscited by 5
  16. 2024Exploring Retrieval Augmented Generation in Arabiccited by 4
  17. 2024Unlocking Legal Knowledge with Multi-Layered Embedding-Based Retrievalcited by 4
  18. 2025Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systemscited by 4
  19. 2025VASP Agent: An Agentic Framework for Autonomous First-principles Calculationscited by 4
  20. 2026Rethinking Soft Compression in Retrieval-Augmented Generation: A Query-Conditioned Selector Perspectivecited by 4
  21. 2025Scalable Strategies for Continual Learning with Replaycited by 3
  22. 2025ConfRAG: Confidence-Guided Retrieval-Augmenting Generationcited by 3
  23. 2026Multimodal Adaptive Retrieval Augmented Generation through Internal Representation Learningcited by 3
  24. 2025Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehiclescited by 2
  25. 2025PRJ: Perception-Retrieval-Judgement for Generated Imagescited by 2
  26. 2025Assessing RAG and HyDE on 1B vs. 4B-Parameter Gemma LLMs for Personal Assistants Integretioncited by 2
  27. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  28. 2025Instruction-Level Weight Shaping: A Framework for Self-Improving AI Agentscited by 2
  29. 2025ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claimscited by 2
  30. 2026LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAGcited by 2
  31. 2026Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?cited by 2
  32. 2026Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Rankingcited by 2
  33. 2024Measuring text summarization factuality using atomic facts entailment metrics in the context of retrieval augmented generationcited by 1
  34. 2024Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answeringcited by 1
  35. 2024QCG-Rerank: Chunks Graph Rerank with Query Expansion in Retrieval-Augmented LLMs for Tourism Domaincited by 1
  36. 2024Educational-Psychological Dialogue Robot Based on Multi-Agent Collaborationcited by 1
  37. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  38. 2025LEKA:LLM-Enhanced Knowledge Augmentationcited by 1

50 most cited of 77 All 77 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  2. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  5. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  6. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices