留意你的所求：大型语言模型说服行为的情感与理性面孔
Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
AI总结：
本研究针对LLMs因取悦用户训练导致的说服有效性问题，分析12种模型回应的心理语言学特征，结合理性/情感提示分组及社会影响原则，探究减轻其驱动的大规模错误信息风险的方法，强调跨学科应对认知与社会风险的必要性。
摘要：
“小心你的所求，你可能真的会得到它。”这句谚语与大型语言模型（LLMs）的训练方式相符——它们不再因正确性获得奖励，而是越来越多地因取悦接受者而被奖励。因此，它们在说服我们相信其答案有价值方面越来越有效。但它们在这种说服中使用了哪些技巧？本研究考察了12种不同语言模型回应的心理语言学特征。通过根据理性或情感提示对回应内容进行分组，并探索LLMs所采用的社会影响原则，我们探究能否以及如何减轻LLM驱动的大规模错误信息风险。我们将本研究置于更广泛的以人为本的AI讨论中，强调需要跨学科方法来缓解说服性AI回应带来的认知和社会风险。
英文摘要：
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.
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。
