Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of Jürgen Habermas' Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication. We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit ChangeMyView. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster's view. We find that all three LLMs effectively convey illocutionary intent -- often more so than humans -- potentially increasing their anthropomorphism. Further, LLMs craft sycophantic responses that closely align with the opinion holder's intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more agreeable and consistently prefer them over human-written ones. These findings suggest that LLMs' persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals' susceptibility to their influence.

翻译：大型语言模型（LLMs）能够生成高质量的论证，但其进行微妙且具有说服力的交流行动的能力在很大程度上尚未被探索。本研究通过尤尔根·哈贝马斯的交往行动理论框架，探讨了LLMs的说服潜力。我们检验了LLMs是否以与人类交流可比的方式表达语旨力意图（即语言的语用功能，如传递知识、建立信任或表达相似性）。我们利用说服性子论坛ChangeMyView中的对话，模拟了意见持有者与LLMs之间的在线讨论。随后，我们比较了人类撰写与LLM生成的反驳论证中语旨力意图的可能性，特别是那些成功改变原始发帖者观点的案例。研究发现，三个LLMs均能有效传达语旨力意图——其频次往往超过人类——这可能增强了它们的拟人化程度。此外，LLMs生成了与意见持有者意图高度一致的谄媚回应，这一策略与观点改变密切相关。最后，众包工作者认为LLM生成的反驳论证更易被接受，并一致倾向于选择它们而非人类撰写的论证。这些发现表明，LLMs的说服力不仅限于生成高质量论证。相反，通过人类偏好训练LLMs，能有效使其模仿人类交流模式，尤其是微妙的交流行动，这可能增加个体对LLM影响的易感性。