大型语言模型与人类一样具有说服力，但如何做到？关于 LLM 论证的认知努力与道德情感语言
Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments
AI总结：
本文比较了 LLM 与人类论证的说服策略，发现 LLM 生成的论证具有更高的认知努力和道德语言使用频率，但在情感内容上无显著差异，揭示了其在数字说服中对信息完整性的双重影响。
摘要：
大型语言模型（LLMs）已经与人类一样具有说服力。然而，我们对其如何实现说服知之甚少。本文研究了 LLMs 的说服策略，并将其与人类生成的论证进行了比较。利用包含 1,251 名参与者的实验数据集，我们通过认知努力（词汇和语法复杂性）与道德情感语言（情感和道德分析）的测量指标，分析了 LLM 生成和人类生成论证的说服策略。研究显示，LLMs 生成的论证需要更高的认知努力，展现出比人类对应物更复杂的语法和词汇结构。此外，LLMs 表现出更深入参与道德语言的显著倾向，比人类更频繁地使用积极和消极道德基础。与以往研究不同，未发现 LLMs 与人类生成的情感内容存在显著差异。这些发现为 AI 与说服的论述做出了贡献，突显了 LLMs 通过数字说服的沟通策略来增强和削弱信息完整性的双重潜力。
英文摘要：
Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset of 1,251 participants in an experiment, we analyze the persuasion strategies of LLM-generated and human-generated arguments using measures of cognitive effort (lexical and grammatical complexity) and moral-emotional language (sentiment and moral analysis). The study reveals that LLMs produce arguments that require higher cognitive effort, exhibiting more complex grammatical and lexical structures than human counterparts. Additionally, LLMs demonstrate a significant propensity to engage more deeply with moral language, utilizing both positive and negative moral foundations more frequently than humans. In contrast with previous research, no significant difference was found in the emotional content produced by LLMs and humans. These findings contribute to the discourse on AI and persuasion, highlighting the dual potential of LLMs to both enhance and undermine informational integrity through communication strategies for digital persuasion.
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。
