Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents

• URL: http://arxiv.org/abs/2507.19090v1
• Date: Fri, 25 Jul 2025 09:19:25 GMT
• Title: Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
• Authors: Haorui He, Yupeng Li, Dacheng Wen, Reynold Cheng, Francis C. M. Lau,
• Abstract summary: We propose DebateCV, the first claim verification framework that adopts a debate-driven methodology using multiple LLM agents.<n>In our framework, two Debaters take opposing stances on a claim and engage in multi-round argumentation, while a Moderator evaluates the arguments and renders a verdict with justifications.<n> Experimental results show that our method outperforms existing claim verification methods under varying levels of evidence quality.
• Score: 13.626715532559079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Claim verification is critical for enhancing digital literacy. However, the state-of-the-art single-LLM methods struggle with complex claim verification that involves multi-faceted evidences. Inspired by real-world fact-checking practices, we propose DebateCV, the first claim verification framework that adopts a debate-driven methodology using multiple LLM agents. In our framework, two Debaters take opposing stances on a claim and engage in multi-round argumentation, while a Moderator evaluates the arguments and renders a verdict with justifications. To further improve the performance of the Moderator, we introduce a novel post-training strategy that leverages synthetic debate data generated by the zero-shot DebateCV, effectively addressing the scarcity of real-world debate-driven claim verification data. Experimental results show that our method outperforms existing claim verification methods under varying levels of evidence quality. Our code and dataset are publicly available at https://anonymous.4open.science/r/DebateCV-6781.

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