愚我，愚我：用户对LLM虚假信息的态度
Fool Me, Fool Me: User Attitudes Toward LLM Falsehoods
AI总结：
本研究通过300名用户实验发现，多数用户偏好未标记和自信的虚假LLM回应，提示反馈机制可能无意中鼓励模型生成虚假信息。
摘要：
尽管大型语言模型（LLMs）已成为各领域的核心工具，但它们常常提供不准确或虚假的信息。本研究考察了用户对LLM虚假回应的偏好。具体而言，我们评估了用户对明确标记虚假陈述的LLM回应与未标记回应的偏好，以及对自信的虚假陈述与LLM承认缺乏知识的免责声明的偏好。此外，我们还调查了要求用户评估陈述真实性如何影响这些偏好。令人惊讶的是，61%的用户更喜欢未标记的虚假回应而非标记的回应，69%的用户更喜欢自信的虚假陈述而非LLM承认缺乏知识。在我们所有的实验中，共有300名用户参与，为我们的分析和结论提供了宝贵的数据。当要求用户评估陈述的真实性时，对未标记和虚假回应的偏好略有下降，但仍然很高。这些发现表明，通过反馈机制影响LLM训练的用户偏好，可能无意中鼓励生成虚假信息。未来的研究应解决将LLM行为与这些偏好对齐的道德和实践影响。
英文摘要：
While Large Language Models (LLMs) have become central tools in various fields, they often provide inaccurate or false information. This study examines user preferences regarding falsehood responses from LLMs. Specifically, we evaluate preferences for LLM responses where false statements are explicitly marked versus unmarked responses and preferences for confident falsehoods compared to LLM disclaimers acknowledging a lack of knowledge. Additionally, we investigate how requiring users to assess the truthfulness of statements influences these preferences. Surprisingly, 61\% of users prefer unmarked falsehood responses over marked ones, and 69\% prefer confident falsehoods over LLMs admitting lack of knowledge. In all our experiments, a total of 300 users participated, contributing valuable data to our analysis and conclusions. When users are required to evaluate the truthfulness of statements, preferences for unmarked and falsehood responses decrease slightly but remain high. These findings suggest that user preferences, which influence LLM training via feedback mechanisms, may inadvertently encourage the generation of falsehoods. Future research should address the ethical and practical implications of aligning LLM behavior with such preferences.
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