Despite their potential as human proxies, LLMs often fail to generate heterogeneous data with human-like diversity, thereby diminishing their value in advancing social science research. To address this gap, we propose a novel method to incorporate psychological insights into LLM simulation through the Personality Structured Interview (PSI). PSI leverages psychometric scale-development procedures to capture personality-related linguistic information from a formal psychological perspective. To systematically evaluate simulation fidelity, we developed a measurement theory grounded evaluation procedure that considers the latent construct nature of personality and evaluates its reliability, structural validity, and external validity. Results from three experiments demonstrate that PSI effectively improves human-like heterogeneity in LLM-simulated personality data and predicts personality-related behavioral outcomes. We further offer a theoretical framework for designing theory-informed structured interviews to enhance the reliability and effectiveness of LLMs in simulating human-like data for broader psychometric research.

翻译：尽管大型语言模型（LLM）具备作为人类代理的潜力，但其在生成具有类人多样性的异质性数据方面往往表现不足，从而削弱了其在推动社会科学研究方面的价值。为弥补这一不足，我们提出了一种新颖方法，通过人格结构化访谈（PSI）将心理学洞见融入LLM模拟。PSI借鉴心理测量学量表开发流程，从形式化心理学视角捕捉与人格相关的语言信息。为系统评估模拟保真度，我们开发了基于测量理论的评估程序，该程序考虑人格的潜在构念本质，并评估其信度、结构效度与外部效度。三项实验结果表明，PSI能有效提升LLM模拟人格数据的类人异质性，并能预测人格相关的行为结果。我们进一步提出了设计理论指导型结构化访谈的理论框架，以增强LLM在模拟类人数据方面的可靠性与有效性，从而服务于更广泛的心理测量学研究。