Large Language Models (LLMs) have been observed to encode and perpetuate harmful associations present in the training data. We propose a theoretically grounded framework called StereoMap to gain insights into their perceptions of how demographic groups have been viewed by society. The framework is grounded in the Stereotype Content Model (SCM); a well-established theory from psychology. According to SCM, stereotypes are not all alike. Instead, the dimensions of Warmth and Competence serve as the factors that delineate the nature of stereotypes. Based on the SCM theory, StereoMap maps LLMs' perceptions of social groups (defined by socio-demographic features) using the dimensions of Warmth and Competence. Furthermore, the framework enables the investigation of keywords and verbalizations of reasoning of LLMs' judgments to uncover underlying factors influencing their perceptions. Our results show that LLMs exhibit a diverse range of perceptions towards these groups, characterized by mixed evaluations along the dimensions of Warmth and Competence. Furthermore, analyzing the reasonings of LLMs, our findings indicate that LLMs demonstrate an awareness of social disparities, often stating statistical data and research findings to support their reasoning. This study contributes to the understanding of how LLMs perceive and represent social groups, shedding light on their potential biases and the perpetuation of harmful associations.

翻译：大型语言模型（LLMs）被观察到会编码并强化训练数据中的有害关联。我们提出一种名为StereoMap的理论驱动框架，以深入理解这些模型对社会群体认知的感知方式。该框架基于心理学领域成熟理论——刻板印象内容模型（SCM）。根据SCM理论，刻板印象并非千篇一律，而是通过“温暖”与“能力”这两个维度来界定其本质特征。StereoMap基于SCM理论，利用温暖与能力维度映射LLMs对社会群体（按社会人口特征定义）的感知。此外，该框架还能探究LLMs判断过程中的关键词与推理表述，揭示其感知背后的潜在影响因素。我们的结果表明，LLMs对这些群体展现出多样化的感知，表现为在温暖与能力维度上的混合评价。进一步分析LLMs的推理过程发现，这些模型具有对社会不平等的认知意识，并常引用统计数据与研究结果来支撑其推理。本研究有助于理解LLMs如何看待和表征社会群体，揭示其潜在偏见及有害关联的强化机制。