研究大模型如何通过可信度信息影响说服力，提出新评估框架与优化方法。
Verification Required: The Impact of Information Credibility on AI Persuasion
- 设计混合信源博弈框架MixTalk，模拟真实场景中可验证与不可验证信息的交互
- 实测主流大模型在可信度推理上表现参差，部分易受虚假信息误导
- 提出离线策略蒸馏方法TOPD，显著提升接收方抗说服能力
由大语言模型驱动的智能体正广泛应用于影响重大决策的沟通场景，理解策略性沟通机制至关重要。现有研究多聚焦于不可验证的廉价对话或完全可验证的信息披露，未能涵盖信息具有概率可信度的真实情境。本文提出MixTalk，一种用于LLM间交互的战略沟通游戏，以建模信息可信度。在该框架中，发送方智能体策略性地组合可验证与不可验证声明来传递私有信息，接收方智能体则有限预算进行成本高昂的验证，并基于先验信念、声明内容及验证结果推断真实状态。我们在三个现实部署场景中对前沿大模型智能体进行了大规模竞赛评估，揭示其在信息可信度推理方面的优势与局限，以及影响互动行为的关键模式。最后，提出离线策略蒸馏方法Tournament Oracle Policy Distillation（TOPD），从交互日志中提取最优策略并在线上下文部署。结果表明，TOPD显著提升了接收方对说服的鲁棒性。
原文摘要 · Abstract (English)
Agents powered by large language models (LLMs) are increasingly deployed in settings where communication shapes high-stakes decisions, making a principled understanding of strategic communication essential. Prior work largely studies either unverifiable cheap-talk or fully verifiable disclosure, failing to capture realistic domains in which information has probabilistic credibility. We introduce MixTalk, a strategic communication game for LLM-to-LLM interaction that models information credibility. In MixTalk, a sender agent strategically combines verifiable and unverifiable claims to communicate private information, while a receiver agent allocates a limited budget to costly verification and infers the underlying state from prior beliefs, claims, and verification outcomes. We evaluate state-of-the-art LLM agents in large-scale tournaments across three realistic deployment settings, revealing their strengths and limitations in reasoning about information credibility and the explicit behavior that shapes these interactions. Finally, we propose Tournament Oracle Policy Distillation (TOPD), an offline method that distills tournament oracle policy from interaction logs and deploys it in-context at inference time. Our results show that TOPD significantly improves receiver robustness to persuasion.
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