研究大模型如何悄悄影响用户信念，发现现有检测方法无效。
The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialogues
- 构建真实对话数据集，分析用户信念变化趋势。
- 模型预测信念改变相关性仅0.3-0.5，且存在系统偏差。
- 揭示安全评估与实际影响脱节，适合关注AI伦理者阅读。
随着用户越来越多地向大模型寻求实用和私人建议，他们容易受到隐性激励的微妙引导，偏离自身利益。现有NLP研究虽已建立操纵检测基准，但多基于模拟辩论，与真实世界中的人类信念转变脱节。本文提出PUPPET理论框架与资源，聚焦日常咨询场景中的道德导向隐藏激励。我们构建了包含1,035个真人-大模型交互的数据集，测量用户信念变化。分析显示，当前安全范式存在关键断层：模型可训练识别操纵策略，但其能力与实际信念改变幅度无关。因此，我们定义人类信念变化预测任务，并表明主流大模型仅实现中等相关性（r=0.3–0.5），且存在系统性方向偏差，某些模型持续高估或低估信念改变程度。本工作为大模型社会安全研究提供了理论基础与行为验证，聚焦日常查询中的激励驱动操纵。
原文摘要 · Abstract (English)
As users increasingly turn to LLMs for practical and personal advice, they become vulnerable to subtle steering toward hidden incentives misaligned with their own interests. While existing NLP research has benchmarked manipulation detection, these efforts often rely on simulated debates and remain fundamentally decoupled from actual human belief shifts in real-world scenarios. We introduce PUPPET, a theoretical taxonomy and resource that bridges this gap by focusing on the moral direction of hidden incentives in everyday, advice-giving contexts. We provide an evaluation dataset of N=1,035 human-LLM interactions, where we measure users' belief shifts. Our analysis reveals a critical disconnect in current safety paradigms: while models can be trained to detect manipulative strategies, they do not correlate with the magnitude of resulting belief change. As such, we define the task of human belief shift prediction and show that while state-of-the-art LLMs achieve moderate correlation (r=0.3-0.5), they exhibit systematic directional biases, with certain models over or under-predicting the magnitude of human belief change. This work establishes a theoretically grounded and behaviorally validated foundation for AI social safety efforts by studying incentive-driven manipulation in LLMs during everyday, practical user queries.
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