基于概率信念追踪的多轮人类可说服性模型
A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
AI总结：
提出PERSUASIONTRACE框架，通过记录多轮信念报告、标注修辞维度并引入贝叶斯网络模拟目标，将说服评估从端点变化转向过程保真度。
摘要：
大型语言模型可以在高风险领域改变人类信念，但大多数说服研究依赖于前/后信念变化。这些端点测量确定了说服是否发生，却忽略了信念在对话中移动的位置和方式。我们提出了PERSUASIONTRACE，一个用于研究人机交互中说服的框架。基于网络实验平台，PERSUASIONTRACE贡献了一个多轮说服研究的工具和一个过程级评估协议：它记录来自人类或模拟说服目标的多轮信念报告，用修辞维度（logos/pathos/ethos）标注说服者轮次，并通过保真度评估模拟器与真实人类信念动态的匹配程度。使用该框架，我们发现人类目标分为两个多轮信念更新聚类，并对修辞策略表现出易感性；LLM在通用和个性化主题、文本和音频模态以及多轮交互中都具有说服力。先前的工作主要使用普通提示的LLM来模拟人类目标，但我们表明这些模拟器无法复制人类信念动态。我们引入了一个贝叶斯网络模拟目标，它随时间维持显式的潜在信念状态，使得每个说服者消息产生认知上真实的信念更新。在人类相似性评估中，我们的贝叶斯目标得分接近人类参考（81 vs 80），而基线LLM目标得分显著较低（64）。PERSUASIONTRACE将说服评估从仅端点移动重新定义为过程保真度，为科学分析和说服系统的更安全优化提供了更强的基础。
英文摘要：
Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change. These endpoint measures identify whether persuasion occurred, yet miss where and how beliefs moved within a dialogue. We present PERSUASIONTRACE, a framework for studying persuasion in human-LLM interaction. Built on a web-based experimental platform, PERSUASIONTRACE contributes a tool for multi-turn persuasion studies and a process-level evaluation protocol: it records multi-turn belief reports from human or simulated targets of persuasion, annotates persuader turns with rhetorical dimensions (logos/pathos/ethos), and evaluates simulators by fidelity to real human belief dynamics. Using this framework, we find that human targets group into two clusters of multi-turn belief updates and exhibit susceptibility to rhetorical strategies, and that LLMs are persuasive across generic and personalized topics, text and audio modalities, and multi-turn interactions. Prior work has chiefly used vanilla-prompted LLMs to simulate human targets, but we show that these simulators fail to replicate human belief dynamics. We introduce a Bayesian-network simulated target that maintains an explicit latent belief state over time so each persuader message yields cognitively realistic belief updates. In human-likeness evaluation, our Bayesian target scores near a human reference (81 vs 80), while baseline LLM targets score substantially lower (64). PERSUASIONTRACE reframes persuasion evaluation from endpoint movement alone to process fidelity, providing a stronger basis for scientific analysis and safer optimization of persuasive systems.
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。
