Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914443410014208 |
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| author | He, Haorui Li, Yupeng Wen, Dacheng Chen, Yang Cheng, Reynold Chen, Donglong Lau, Francis C. M. |
| author_facet | He, Haorui Li, Yupeng Wen, Dacheng Chen, Yang Cheng, Reynold Chen, Donglong Lau, Francis C. M. |
| contents | State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fact-checkers, we propose \textbf{DebateCV}, the first debate-driven claim verification framework powered by multiple LLM agents. In DebateCV, two \textit{Debaters} argue opposing stances to surface subtle errors in single-agent assessments. A decisive \textit{Moderator} is then required to weigh the evidential strength of conflicting arguments to deliver an accurate verdict. Yet, zero-shot Moderators are biased toward neutral judgments, and no datasets exist for training them. To bridge this gap, we propose \textbf{Debate-SFT}, a post-training framework that leverages synthetic data to enhance agents' ability to effectively adjudicate debates for claim verification. Results show that our methods surpass state-of-the-art non-debate approaches in both accuracy (across various evidence conditions) and justification quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents He, Haorui Li, Yupeng Wen, Dacheng Chen, Yang Cheng, Reynold Chen, Donglong Lau, Francis C. M. Computation and Language State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fact-checkers, we propose \textbf{DebateCV}, the first debate-driven claim verification framework powered by multiple LLM agents. In DebateCV, two \textit{Debaters} argue opposing stances to surface subtle errors in single-agent assessments. A decisive \textit{Moderator} is then required to weigh the evidential strength of conflicting arguments to deliver an accurate verdict. Yet, zero-shot Moderators are biased toward neutral judgments, and no datasets exist for training them. To bridge this gap, we propose \textbf{Debate-SFT}, a post-training framework that leverages synthetic data to enhance agents' ability to effectively adjudicate debates for claim verification. Results show that our methods surpass state-of-the-art non-debate approaches in both accuracy (across various evidence conditions) and justification quality. |
| title | Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.19090 |