mlsanityData to 14 Oct 2026

Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation

arXiv 2410.05801 ↗First posted 8 Oct 2024cs.CL cs.AIcited by 12

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

also in 1 more
Abstract (arXiv)

Recent Retrieval Augmented Generation (RAG) aims to enhance Large Language Models (LLMs) by incorporating extensive knowledge retrieved from external sources. However, such approach encounters some challenges: Firstly, the original queries may not be suitable for precise retrieval, resulting in erroneous contextual knowledge; Secondly, the language model can easily generate inconsistent answer with external references due to their knowledge boundary limitation. To address these issues, we propose the chain-of-verification (CoV-RAG) to enhance the external retrieval correctness and internal generation consistency. Specifically, we integrate the verification module into the RAG, engaging in scoring, judgment, and rewriting. To correct external retrieval errors, CoV-RAG retrieves new knowledge using a revised query. To correct internal generation errors, we unify QA and verification tasks with a Chain-of-Thought (CoT) reasoning during training. Our comprehensive experiments across various LLMs demonstrate the effectiveness and adaptability compared with other strong baselines. Especially, our CoV-RAG can significantly surpass the state-of-the-art baselines using different LLM backbones.

Key facts. Cited by 12 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 35 of its 46 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

35 of 46 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions20 Dec

Other references · 34

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Let's Verify Step by Stepcited by 3,789
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  8. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  9. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  10. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  11. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  12. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
22 more
  1. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  2. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  5. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  6. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  7. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 287
  8. 2023Do Large Language Models Know What They Don't Know?cited by 243
  9. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  10. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  11. 2023Query2doc: Query Expansion with Large Language Modelscited by 149
  12. 2023Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentationcited by 147
  13. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 130
  14. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 124
  15. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  16. 2023WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferencescited by 100
  17. 2022DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answeringcited by 96
  18. 2023Cognitive Mirage: A Review of Hallucinations in Large Language Modelscited by 84
  19. 2023Learning to Filter Context for Retrieval-Augmented Generationcited by 83
  20. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 72
  21. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 64
  22. 2023Knowledge-Augmented Language Model Verificationcited by 22

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 3.

12 papers.

  1. 2025Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcementcited by 11
  2. 2025LatentEvolve: Self-Evolving Test-Time Scaling in Latent Spacecited by 9
  3. 2025Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premisescited by 9
  4. 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AIcited by 8
  5. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 6
  6. 2025Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learningcited by 4
  7. 2025Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verificationcited by 2
  8. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  9. 2025Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximationcited by 1
  10. 2026Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflictscited by 1
  11. 2026Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignmentcited by 0
  12. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
  2. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
  3. 2025Dynamic and Parametric Retrieval-Augmented Generation
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  5. 2026Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
  6. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  7. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
  8. 2024Semantic Tokens in Retrieval Augmented Generation