Proceedings

Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs

Ziyu Ge, Yuhao Wu, Daniel Wai Kit Chin, Roy Ka-Wei Lee, Rui Cao

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 9656-9664.

https://doi.org/10.24963/ijcai.2025/1073

Keywords

  • Uncertainty in AI: General
  • AI Ethics, Trust, Fairness: General
  • Natural Language Processing: General

Abstract

Large Language Models (LLMs) augmented with retrieval mechanisms have demonstrated significant potential in fact-checking tasks by integrating external knowledge. However, their reliability decreases when confronted with conflicting evidence from sources of varying credibility. This paper presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) models for fact-checking in the presence of conflicting evidence. To support this study, we introduce CONFACT (Conflicting Evidence for Fact-Checking), a novel dataset comprising questions paired with conflicting information from various sources. Extensive experiments reveal critical vulnerabilities in state-of-the-art RAG methods, particularly in resolving conflicts stemming from differences in media source credibility. To address these challenges, we investigate strategies to integrate media background information into both the retrieval and generation stages. Our results show that effectively incorporating source credibility significantly enhances the ability of RAG models to resolve conflicting evidence and improve fact-checking performance.

Ziyu Ge, Yuhao Wu, Daniel Wai Kit Chin, Roy Ka-Wei Lee, and Rui Cao. 2025. Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9656–9664. https://doi.org/10.24963/ijcai.2025/1073
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