人们会同意我的想法：探究大语言模型的虚假共识效应
People will agree what I think: Investigating LLM's False Consensus Effect
AI总结：
本文通过两项研究考察大语言模型中的虚假共识效应，发现流行LLMs普遍存在该认知偏差，并揭示了不同提示风格下FCE增强或减弱的具体条件。
摘要：
大语言模型（LLMs）近期已被应用于需要沟通的交互系统中。由于模型中的错误信念可能损害此类系统的可用性，LLMs不应具有人类所存在的认知偏差。心理学家尤其关注虚假共识效应（False Consensus Effect，FCE），这是一种个体高估他人与自己信念或行为一致程度的认知偏差，因为FCE会通过形成错误信念而干扰顺畅沟通。然而，以往研究对LLMs中的FCE考察不够充分，需要更多考虑混杂偏差、一般情境以及提示变化。因此，在本文中，我们开展两项研究来考察LLMs中的FCE现象。在研究1中，我们探究LLMs是否具有FCE。在研究2中，我们探索不同提示风格如何影响FCE的表现。研究结果表明，流行的LLMs具有FCE。同时，结果明确了与正常使用相比，FCE在何种条件下会变得更强或更弱。
英文摘要：
Large Language Models (LLMs) have been recently adopted in interactive systems requiring communication. As the false belief in a model can harm the usability of such systems, LLMs should not have cognitive biases that humans have. Psychologists especially focus on the False Consensus Effect (FCE), a cognitive bias where individuals overestimate the extent to which others share their beliefs or behaviors, because FCE can distract smooth communication by posing false beliefs. However, previous studies have less examined FCE in LLMs thoroughly, which needs more consideration of confounding biases, general situations, and prompt changes. Therefore, in this paper, we conduct two studies to examine the FCE phenomenon in LLMs. In Study 1, we investigate whether LLMs have FCE. In Study 2, we explore how various prompting styles affect the demonstration of FCE. As a result of these studies, we identified that popular LLMs have FCE. Also, the result specifies the conditions when FCE becomes more or less prevalent compared to normal usage.
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