mlsanityData to 7 Oct 2026

Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate

arXiv 2402.07401 ↗First posted 12 Feb 2024cs.CLcited by 48

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs) excel in text generation, their capability for producing faithful explanations in fact-checking remains underexamined. Our study investigates LLMs' ability to generate such explanations, finding that zero-shot prompts often result in unfaithfulness. To address these challenges, we propose the Multi-Agent Debate Refinement (MADR) framework, leveraging multiple LLMs as agents with diverse roles in an iterative refining process aimed at enhancing faithfulness in generated explanations. MADR ensures that the final explanation undergoes rigorous validation, significantly reducing the likelihood of unfaithful elements and aligning closely with the provided evidence. Experimental results demonstrate that MADR significantly improves the faithfulness of LLM-generated explanations to the evidence, advancing the credibility and trustworthiness of these explanations.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 20 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

14 of 20 references.

References · 14

  1. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  3. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  4. 2021A Survey on Automated Fact-Checkingcited by 376
  5. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 209
  6. 2020Generating Fact Checking Explanationscited by 132
  7. 2021Annotating and Modeling Fine-grained Factuality in Summarizationcited by 131
  8. 2020GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Mediacited by 108
  9. 2023Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioningcited by 47
  10. 2023Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articlescited by 45
  11. 2020Multi-Hop Fact Checking of Political Claimscited by 37
  12. 2023Zero-shot Faithful Factual Error Correctioncited by 26
2 more
  1. 2023Benchmarking the Generation of Fact Checking Explanationscited by 25
  2. 2019Explainable Fact Checking with Probabilistic Answer Set Programmingcited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 19; 2025: 21; 2026: 8.

48 papers.

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By area:

  1. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  2. 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discoverycited by 74
  3. 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 52
  4. 2025Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Abovecited by 51
  5. 2025A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoningcited by 40
  6. 2024Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generationcited by 29
  7. 2025Can LLM Agents Really Debate? A Controlled Study of Multi-Agent Debate in Logical Reasoningcited by 27
  8. 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 23
  9. 2024Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMscited by 22
  10. 2025Do We Truly Need So Many Samples? Multi-LLM Repeated Sampling Efficiently Scales Test-Time Computecited by 20
  11. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  12. 2024Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detectioncited by 18
36 more
  1. 2024Plurals: A System for Guiding LLMs Via Simulated Social Ensemblescited by 17
  2. 2024Training Language Models to Win Debates with Self-Play Improves Judge Accuracycited by 16
  3. 2025MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructionscited by 16
  4. 2025GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checkingcited by 12
  5. 2024Evaluating Moral Beliefs across LLMs through a Pluralistic Frameworkcited by 11
  6. 2024Customized large language models can outperform Community Notes in correcting misinformationcited by 9
  7. 2024STORYSUMM: Evaluating Faithfulness in Story Summarizationcited by 9
  8. 2025MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaborationcited by 9
  9. 2024Epistemic Integrity in Large Language Modelscited by 7
  10. 2025Evaluating open-source Large Language Models for automated fact-checkingcited by 6
  11. 2025Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agentscited by 6
  12. 2024Automating IRAC Analysis in Malaysian Contract Law using a Semi-Structured Knowledge Basecited by 5
  13. 2025Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learningcited by 5
  14. 2026Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoringcited by 5
  15. 2024AMREx: AMR for Explainable Fact Verificationcited by 4
  16. 2025Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Modelscited by 4
  17. 2024Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checkingcited by 3
  18. 2024CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Redditcited by 3
  19. 2025EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actionscited by 3
  20. 2025Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasioncited by 3
  21. 2024Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Frameworkcited by 2
  22. 2025IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Datacited by 2
  23. 2025Adaptive Multi-Agent Response Refinement in Conversational Systemscited by 2
  24. 2026GitSearch: Enhancing Community Notes Generation with Gap-Informed Targeted Searchcited by 2
  25. 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Modelscited by 2
  26. 2024Generative Debunking of Climate Misinformationcited by 1
  27. 2025CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMscited by 1
  28. 2025REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Controlcited by 1
  29. 2026Multimodal Information Fusion for Chart Understanding: A Survey of MLLMs -- Evolution, Limitations, and Cognitive Enhancementcited by 1
  30. 2026RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 1
  31. 2024ViMGuard: A Novel Multi-Modal System for Video Misinformation Guardingcited by 0
  32. 2025Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checkingcited by 0
  33. 2025RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 0
  34. 2026Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Datasetcited by 0
  35. 2026BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debatecited by 0
  36. 2026A Theory of Post-hoc Debate Judgementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023The Perils & Promises of Fact-checking with Large Language Models
  2. 2025FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models
  3. 2026Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval
  4. 2026Tool-MAD: A Multi-Agent Debate Framework for Fact Verification with Diverse Tool Augmentation and Adaptive Retrieval
  5. 2025MAD-Fact: A Multi-Agent Debate Framework for Long-Form Factuality Evaluation in LLMs
  6. 2025Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework
  7. 2026Evaluating the False Trust Engendered by LLM Explanations
  8. 2024Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models