mlsanityData to 14 Oct 2026

Corrective Retrieval Augmented Generation

arXiv 2401.15884 ↗First posted 29 Jan 2024cs.CLcited by 197

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

Abstract (arXiv)

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return sub-optimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches.

Key facts. Cited by 197 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 27 of its 42 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

27 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  11. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
14 more
  1. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  2. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  3. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  4. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 748
  5. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  6. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 632
  7. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 488
  8. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  9. 2021Internet-Augmented Dialogue Generationcited by 217
  10. 2023Generating Benchmarks for Factuality Evaluation of Language Modelscited by 105
  11. 2021Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challengecited by 80
  12. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  13. 2022DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generationcited by 19
  14. 2022TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledgecited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 43; 2025: 59; 2026: 95.

197 papers.

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  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  2. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 245
  3. 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learningcited by 161
  4. 2024Certifiably Robust RAG against Retrieval Corruptioncited by 151
  5. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGcited by 123
  6. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 101
  7. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Draftingcited by 77
  8. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 72
  9. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  10. 2024Inference Scaling for Long-Context Retrieval Augmented Generationcited by 65
  11. 2024Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Modelscited by 58
  12. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Modelscited by 43
38 more
  1. 2024Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agentscited by 35
  2. 2025Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoningcited by 35
  3. 2024RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewardscited by 33
  4. 2025Generative to Agentic AI: Survey, Conceptualization, and Challengescited by 33
  5. 2024Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Trackcited by 32
  6. 2024How Much Can RAG Help the Reasoning of LLM?cited by 31
  7. 2024Improving Retrieval Augmented Language Model with Self-Reasoningcited by 28
  8. 2025Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievalscited by 28
  9. 2024Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selectioncited by 25
  10. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Modelscited by 25
  11. 2024Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Basescited by 23
  12. 2024Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Taskscited by 22
  13. 2024Atomic Fact Decomposition Helps Attributed Question Answeringcited by 21
  14. 2025UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularitiescited by 20
  15. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  16. 2025ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Searchcited by 18
  17. 2025Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoningcited by 17
  18. 2024Compressing Long Context for Enhancing RAG with AMR-based Concept Distillationcited by 16
  19. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 16
  20. 2025CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answeringcited by 15
  21. 2025Graph RAG-Tool Fusioncited by 15
  22. 2025AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understandingcited by 15
  23. 2026EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agentscited by 15
  24. 2026Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflictcited by 15
  25. 2024Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoningcited by 14
  26. 2024Accelerating Retrieval-Augmented Generationcited by 14
  27. 2026Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answeringcited by 14
  28. 2024CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Controlcited by 13
  29. 2025ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generationcited by 13
  30. 2024R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generationcited by 12
  31. 2025Credible Plan-Driven RAG Method for Multi-Hop Question Answeringcited by 12
  32. 2025Iterative Self-Incentivization Empowers Large Language Models as Agentic Searcherscited by 11
  33. 2024Preference-Guided Refactored Tuning for Retrieval Augmented Code Generationcited by 10
  34. 2024LLM Agent for Fire Dynamics Simulationscited by 10
  35. 2025Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM Systemcited by 10
  36. 2024ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Modelscited by 9
  37. 2024Evaluating the External and Parametric Knowledge Fusion of Large Language Modelscited by 9
  38. 2025AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generationcited by 9

50 most cited of 197 All 197 (JSON)

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  1. 2024Retrieval-Augmented Generation with Estimation of Source Reliability
  2. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  3. 2024Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
  4. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  5. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  6. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  7. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  8. 2024R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation