mlsanityData to 7 Oct 2026

Removal of Hallucination on Hallucination: Debate-Augmented RAG

arXiv 2505.18581 ↗First posted 24 May 2025cs.CL cs.AIcited by 29

On the map: Retrieval-augmented generation → Hallucination detection in RAG

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) enhances factual accuracy by integrating external knowledge, yet it introduces a critical issue: erroneous or biased retrieval can mislead generation, compounding hallucinations, a phenomenon we term Hallucination on Hallucination. To address this, we propose Debate-Augmented RAG (DRAG), a training-free framework that integrates Multi-Agent Debate (MAD) mechanisms into both retrieval and generation stages. In retrieval, DRAG employs structured debates among proponents, opponents, and judges to refine retrieval quality and ensure factual reliability. In generation, DRAG introduces asymmetric information roles and adversarial debates, enhancing reasoning robustness and mitigating factual inconsistencies. Evaluations across multiple tasks demonstrate that DRAG improves retrieval reliability, reduces RAG-induced hallucinations, and significantly enhances overall factual accuracy. Our code is available at https://github.com/Huenao/Debate-Augmented-RAG.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 37 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

30 of 37 references.

References · 30

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  10. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  11. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  12. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
18 more
  1. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  2. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  3. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  4. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  5. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  6. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  7. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  9. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  10. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  11. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  12. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  13. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 165
  14. 2023Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communicationcited by 78
  15. 2024SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMscited by 67
  16. 2024Multi-Agent Large Language Models for Conversational Task-Solvingcited by 24
  17. 2024LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generationcited by 17
  18. 2024A Picture Is Worth a Graph: A Blueprint Debate Paradigm for Multimodal Reasoningcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 20.

29 papers.

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  1. 2025CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Modelscited by 17
  2. 2025Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agentscited by 6
  3. 2025Silence is Golden: Mitigating Hallucinations in Large Audio-Language Models via Layer-Weighted Vector Steeringcited by 6
  4. 2026GRACE: Reinforcement Learning for Grounded Response and Abstention under Contextual Evidencecited by 6
  5. 2026Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTScited by 5
  6. 2026Courtroom-Style Multi-Agent Debate with Progressive RAG and Role-Switching for Controversial Claim Verificationcited by 4
  7. 2025HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Storescited by 3
  8. 2026Multi-Agent Debate Strategies: Survey, Taxonomy, and Challengescited by 3
  9. 2026To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAGcited by 2
  10. 2025Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuningcited by 1
  11. 2025REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Controlcited by 1
  12. 2026Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinationscited by 1
17 more
  1. 2026Mitigating Hallucination on Hallucination in RAG via Ensemble Votingcited by 1
  2. 2026Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Designcited by 1
  3. 2026LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AIcited by 1
  4. 2026Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrievalcited by 1
  5. 2026Mixture of Debaters: Learn to Debate at Architectural Level in Multi-Agent Reasoningcited by 1
  6. 2026Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoningcited by 1
  7. 2025GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Frameworkcited by 0
  8. 2025Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignmentcited by 0
  9. 2025Exploring Health Misinformation Detection with Multi-Agent Debatecited by 0
  10. 2026Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debatecited by 0
  11. 2026Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futurescited by 0
  12. 2026Why Retrieval-Augmented Generation Fails: A Graph Perspectivecited by 0
  13. 2026Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agentscited by 0
  14. 2026Beyond Topical Similarity: Contrastive Evidence Retrieval with Interpretable Attention Alignment in RAGcited by 0
  15. 2026Memory Makes the Difference: Evaluating How Different Memory Roles Shape Conversational Agentscited by 0
  16. 2026DeAR: Decentralized Agentic Reasoning via Capability Grounding and Collaborative Thought Navigationcited by 0
  17. 2026Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognitioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards
  2. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
  3. 2026RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
  4. 2024RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
  5. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
  6. 2025FVA-RAG: Falsification-Verification Alignment for Mitigating Sycophantic Hallucinations
  7. 2025LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals
  8. 2026Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation