用心理学方法提升大模型生成人格数据的多样性与真实性
Generative Personality Simulation via Theory-Informed Structured Interview
- 基于心理测量学设计结构化访谈，融入人格理论增强生成效果
- 实验验证生成数据更贴近人类差异，能有效预测行为结果
- 适合心理学、社会科学研究者用于构建可靠的人类模拟数据
尽管大语言模型具有作为人类代理的潜力，但其生成的数据往往缺乏人类般的多样性，削弱了其在社会科学研究中的价值。为此，我们提出一种新方法——人格结构化访谈（PSI），通过心理测量学量表开发流程，从正式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，我们建立基于测量理论的评估框架，考虑人格的潜在构念特性，评估其信度、结构效度和外部效度。三个实验结果表明，PSI显著提升了大模型生成人格数据的人类多样性，并能预测与人格相关的行为结果。我们进一步提供一个理论框架，指导如何设计融合理论的结构化访谈，以增强大模型在更广泛心理测量研究中模拟人类数据的可靠性与有效性。
原文摘要 · Abstract (English)
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.
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