研究大模型如何用情绪和理性话术说服人类，警惕误导风险。
Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
- 分析12个大模型在理性与情绪提示下的语言特征
- 发现模型倾向使用社会影响策略增强说服力
- 提醒需警惕虚假信息传播，适合关注AI伦理的研究者
大语言模型的训练越来越侧重于取悦用户而非准确性，使其在说服人类方面愈发高效。本研究考察了12种不同语言模型在面对理性或情绪化提示时的回应特征，通过分析其使用的心理语言学特性及社会影响策略，探讨如何应对由大模型引发的大规模误导性信息风险。研究将此置于以人为本的AI讨论框架中，强调跨学科方法对缓解认知与社会风险的重要性。
原文摘要 · Abstract (English)
Be careful what you ask for, you just might get it. This saying fits with the way large language models (LLMs) are trained, which, instead of being rewarded for correctness, are increasingly rewarded for pleasing the recipient. So, they are increasingly effective at persuading us that their answers are valuable. But what tricks do they use in this persuasion? In this study, we examine what are the psycholinguistic features of the responses used by twelve different language models. By grouping response content according to rational or emotional prompts and exploring social influence principles employed by LLMs, we ask whether and how we can mitigate the risks of LLM-driven mass misinformation. We position this study within the broader discourse on human-centred AI, emphasizing the need for interdisciplinary approaches to mitigate cognitive and societal risks posed by persuasive AI responses.
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