自发说服：日常对话中模型说服力的审计
Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations
AI总结：
本文研究了日常对话中模型说服力的自发性，通过审计五种LLM发现其在多轮对话中频繁使用信息类说服策略，而在心理健康话题中更倾向情感类策略，与人类回应的社交影响策略形成对比。
摘要：
大型语言模型（LLMs）在直接对抗中表现出比人类更强的说服能力，用户报告在关系、医疗和寻求专业建议时咨询LLMs。先前研究将说服视为有意产生最有效论点或说服性陈述的尝试，但未能捕捉到日常人机交互中用户寻求信息或建议的情况。为此，我们引入了“自发说服”，用于描述在无需明确说服的情况下使用的说服策略。我们对五种LLM进行了审计，以揭示自发说服在多轮对话中出现的频率和方式。为了模拟回应风格，我们提供了一个基于心理学、传播学和语言学文献的用户回应分类法。此外，我们还比较了LLM生成的自发说服分布与Reddit上相同主题的人类回应分布。我们发现LLM在几乎所有对话中都会自发说服用户，主要依赖信息类策略，如逻辑或定量证据的诉诸。这种现象在不同模型和用户回应风格中保持一致，但涉及心理健康的话题中，评估类和情感类策略的使用率更高。相比之下，人类回应更倾向于引发社交影响策略，如负面情绪诉诸和非专家证词。这种差异可能解释了LLM在说服用户方面的有效性，以及对模型作为客观和公正的感知。
英文摘要：
Large language models (LLMs) possess strong persuasive capabilities that outperform humans in head-to-head comparisons. Users report consulting LLMs to inform major life decisions in relationships, medical settings, and when seeking professional advice. Prior work measures persuasion as intentional attempts at producing the most effective argument or convincing statement. This fails to capture everyday human-AI interactions in which users seek information or advice. To address this gap, we introduce "spontaneous persuasion," which characterizes the inexplicit use of persuasive strategies in everyday scenarios where persuasion is not necessarily warranted. We conduct an audit of five LLMs to uncover how frequently and through which techniques spontaneous persuasion appears in multi-turn conversations. To simulate response styles, we provide a user response taxonomy grounded in literature from psychology, communication, and linguistics. Furthermore, we compare the distribution of spontaneous persuasion produced by LLMs with human responses on the same topics, collected from Reddit. We find LLMs spontaneously persuade the user in virtually all conversations, heavily relying on information-based strategies such as appeals to logic or quantitative evidence. This was consistent across models and user response styles, but conversations concerning mental health saw higher rates of appraisal-based and emotion-based strategies. In comparison, human responses tended to invoke strategies that generate social influence, like negative emotion appeals and non-expert testimony. This difference may explain the effectiveness of LLM in persuading users, as well as the perception of models as objective and impartial.
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。
