用多个AI辩论验证真假，提升复杂声明的判断准确率。
Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
- 让两个AI分别持正反立场辩论，暴露单个AI的判断漏洞。
- 新方法在多种证据条件下准确率超越现有非辩论模型。
- 适合需要高可信度事实核查的研究者与平台使用。
当前最先进的单智能体声明验证方法在需要多维度分析的复杂声明上表现不佳。受现实专业核实人员启发，我们提出首个基于多大语言模型代理的辩论式验证框架 DebateCV。在该框架中，两名辩论者分别从对立角度论证，以揭示单个智能体评估中的细微错误；随后由一名裁判者权衡相互矛盾的论据强度，给出最终结论。然而，零样本裁判者倾向于中立判断，且缺乏用于训练的专用数据集。为此，我们提出 Debate-SFT，一种利用合成数据进行后训练的框架，以增强代理有效裁决辩论的能力。实验表明，所提方法在准确率（涵盖多种证据条件）和理由质量方面均超越现有非辩论类方法。
原文摘要 · Abstract (English)
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.
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