Recent studies suggest large language models (LLMs) can exhibit human-like reasoning, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Almost all advanced approaches fail to replicate human behavior distributions across many models, except in one case involving fine-tuning using a substantial amount of human behavior data. Causes of failure are diverse, relating to input language, roles, and safeguarding. These results caution against using LLMs to study human behaviors or as human surrogates.

本研究探讨了大型语言模型（LLMs）在社会科学研究中作为人类替代品的使用问题，指出LLMs与人类的根本差异及其在模拟人类行为时的局限性。研究发现，除非通过大量人类行为数据进行微调，否则LLMs在模拟人类行为分布方面几乎都存在失败，提醒研究人员谨慎使用LLMs来研究人类行为。

在将大语言模型用作人类替代品时需谨慎：曙光中的斯库拉