人格设定（persona）足以表征个性吗？利用ChatGPT从简单描述中重构智能体的潜在人格
Is persona enough for personality? Using ChatGPT to reconstruct an agent's latent personality from simple descriptions
AI总结：
本研究探讨大语言模型仅通过含社会人口与人格类型的简单描述重构人格的能力，采用HEXACO框架验证了其重构一致性，同时发现偏差与人口因素的影响，为LLM构建智能体模拟系统提供参考并指出研究方向。
摘要：
人格是人类认知的基本组成部分，包含一系列影响行为、思想和情绪的特质。本文探讨了大语言模型（LLM）仅依据包含社会人口统计信息和人格类型信息的简单描述，重构这些复杂认知属性的能力。本研究采用HEXACO人格框架，检验了LLM从简单描述中恢复并预测潜在人格维度的一致性。实验显示，人格重构具备相当程度的一致性，但也存在一些不一致性与偏差，例如在缺乏明确信息时倾向于默认采用积极特质。此外，年龄、子女数量等社会人口统计因素也会影响重构出的人格维度。这些发现对利用LLM构建复杂的基于智能体的模拟系统具有启示意义，同时凸显了进一步研究LLM中鲁棒人格生成的必要性。
英文摘要：
Personality, a fundamental aspect of human cognition, contains a range of traits that influence behaviors, thoughts, and emotions. This paper explores the capabilities of large language models (LLMs) in reconstructing these complex cognitive attributes based only on simple descriptions containing socio-demographic and personality type information. Utilizing the HEXACO personality framework, our study examines the consistency of LLMs in recovering and predicting underlying (latent) personality dimensions from simple descriptions. Our experiments reveal a significant degree of consistency in personality reconstruction, although some inconsistencies and biases, such as a tendency to default to positive traits in the absence of explicit information, are also observed. Additionally, socio-demographic factors like age and number of children were found to influence the reconstructed personality dimensions. These findings have implications for building sophisticated agent-based simulacra using LLMs and highlight the need for further research on robust personality generation in LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
