说服应该是双盲的：基于因果心智理论的多领域对话数据集
Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind
AI总结：
提出基于因果心智理论的多智能体框架ToMMA构建双盲说服对话数据集CToMPersu，以解决现有数据集信息泄露问题，提升对话真实性和说服力。
摘要：
说服性对话是人类交流的核心，但现有数据集通常依赖单一语言模型生成两个角色，产生违反说服双盲性质的不真实交互。为克服这一问题，我们提出ToMMA，一个由因果心智理论引导的多智能体框架，强制角色分离并防止信息泄露。利用ToMMA，我们构建了CToMPersu，一个大规模多轮、多领域数据集，捕捉真实的说服动态。自动评估显示，CToMPersu比先前数据集产生更连贯和更有说服力的对话。此外，当作为知识库使用时，CToMPersu显著增强了大型语言模型的说服性能，自动评估和人工评估均证实了这一点。
英文摘要：
Persuasive dialogue is central to human communication, yet existing datasets often rely on a single language model generating both roles, producing unrealistic interactions that violate the double-blind nature of persuasion. To overcome this, we propose ToMMA, a multi-agent framework guided by causal Theory of Mind that enforces role separation and prevents information leakage. Using ToMMA, we build CToMPersu, a large-scale multi-turn, multi-domain dataset capturing realistic persuasion dynamics. Automatic evaluations show that CToMPersu produces more coherent and persuasive dialogues than prior datasets. Furthermore, when used as a knowledge base, CToMPersu significantly enhances the persuasive performance of large language models, as confirmed by both automatic and human evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
