As Large Language Models (LLMs) become increasingly integrated into our everyday lives, understanding their ability to comprehend human mental states becomes critical for ensuring effective interactions. However, despite the recent attempts to assess the Theory-of-Mind (ToM) reasoning capabilities of LLMs, the degree to which these models can align with human ToM remains a nuanced topic of exploration. This is primarily due to two distinct challenges: (1) the presence of inconsistent results from previous evaluations, and (2) concerns surrounding the validity of existing evaluation methodologies. To address these challenges, we present a novel framework for procedurally generating evaluations with LLMs by populating causal templates. Using our framework, we create a new social reasoning benchmark (BigToM) for LLMs which consists of 25 controls and 5,000 model-written evaluations. We find that human participants rate the quality of our benchmark higher than previous crowd-sourced evaluations and comparable to expert-written evaluations. Using BigToM, we evaluate the social reasoning capabilities of a variety of LLMs and compare model performances with human performance. Our results suggest that GPT4 has ToM capabilities that mirror human inference patterns, though less reliable, while other LLMs struggle.

翻译：随着大型语言模型（LLMs）日益融入日常生活，理解其洞悉人类心智状态的能力成为确保有效交互的关键。尽管近期已有研究尝试评估LLMs的心理理论（ToM）推理能力，但这些模型能在多大程度上与人类ToM对齐仍是一个值得深入探讨的微妙课题。这主要源于两个独特挑战：（1）以往评估结果存在不一致性，（2）现有评估方法的有效性有待商榷。为应对这些挑战，我们提出一种通过填充因果模板程序化生成评估任务的新框架。基于该框架，我们为LLMs构建了包含25个控制项和5000个模型编写评估任务的全新社会推理基准（BigToM）。研究发现，人类参与者对该基准的质量评分优于以往众包评估任务，且与专家编写的评估任务相当。借助BigToM，我们评估了多种LLMs的社会推理能力，并将模型表现与人类性能进行对比。结果表明：GPT4具备与人类推理模式相仿的ToM能力（尽管稳定性稍逊），而其他LLMs则表现欠佳。