将心理测量学应用于大语言模型模拟人群：用生成式智能体重现HEXACO人格量表实验
Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agents
AI总结：
本文通过让310个GPT-4生成式智能体完成HEXACO人格量表并进行因子分析，验证了基于人设的智能体在模拟人类人格结构上的有效性，同时揭示了模型特定偏差，为社会科学研究中使用生成式智能体提供了设计指导。
摘要：
由大语言模型驱动的生成式智能体通过复杂的自然语言交互展现出类人特征。它们能够基于预定义的角色传记来承担角色和人格，这使其成为社会科学研究中人类参与者的低成本替代方案。本文探讨了此类基于人设的智能体在代表人类人群方面的有效性；我们重现了HEXACO人格量表实验，对310个由GPT-4驱动的智能体进行了调查，对其回答进行了因子分析，并将这些结果与Ashton、Lee和Goldberg于2004年发表的原始发现进行了比较。我们的结果发现：1）从智能体的回答中可以恢复出一个连贯且可靠的人格结构，显示出与HEXACO框架的部分一致性；2）当与经过充分筛选的人群相结合时，在GPT-4内部推导出的人格维度具有一致性和可靠性；3）跨模型分析揭示了人格画像的变异性，表明存在模型特定的偏差和局限性。我们讨论了实验过程中遇到的实际考虑因素和挑战。本研究为关于在社会科学研究中使用生成式智能体的潜在益处和局限性的持续讨论做出了贡献，并为设计一致且具有代表性的人设以最大化人类人格特质的覆盖范围和代表性提供了有用的指导。
英文摘要：
Generative agents powered by Large Language Models demonstrate human-like characteristics through sophisticated natural language interactions. Their ability to assume roles and personalities based on predefined character biographies has positioned them as cost-effective substitutes for human participants in social science research. This paper explores the validity of such persona-based agents in representing human populations; we recreate the HEXACO personality inventory experiment by surveying 310 GPT-4 powered agents, conducting factor analysis on their responses, and comparing these results to the original findings presented by Ashton, Lee, & Goldberg in 2004. Our results found 1) a coherent and reliable personality structure was recoverable from the agents' responses demonstrating partial alignment to the HEXACO framework. 2) the derived personality dimensions were consistent and reliable within GPT-4, when coupled with a sufficiently curated population, and 3) cross-model analysis revealed variability in personality profiling, suggesting model-specific biases and limitations. We discuss the practical considerations and challenges encountered during the experiment. This study contributes to the ongoing discourse on the potential benefits and limitations of using generative agents in social science research and provides useful guidance on designing consistent and representative agent personas to maximise coverage and representation of human personality traits.
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。
