构建首个基于因果心智理论的多领域说服对话数据集，让对话更真实可信。
Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind
- 用因果心智理论实现对话双方角色隔离，杜绝信息泄露。
- 数据集包含多轮多领域对话，生成效果优于已有数据集。
- 可显著提升大模型说服力，适合研究对话与人类行为建模者使用。
说服性对话是人类交流的核心，但现有数据集通常由单一语言模型生成双方角色，导致互动不真实且违背说服的双盲特性。为此，我们提出基于因果心智理论的多智能体框架ToMMA，实现角色分离并防止信息泄露。利用ToMMA，我们构建了大规模、多轮、多领域的对话数据集CToMPersu，真实捕捉说服动态。自动评估显示，CToMPersu生成的对话在连贯性和说服力上均优于现有数据集。此外，作为知识库使用时，它能显著提升大语言模型的说服性能，经自动与人工评估验证。
原文摘要 · Abstract (English)
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.
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