模型人格仿真效果取决于角色细节，越详细越真实。
Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
- 构建端到端评估框架，分析个体与群体层面的人格稳定性。
- 发现角色描述越详细，模型模拟人格越逼真，存在规模效应。
- 为社会科学研究中的大模型应用提供可量化的评估方法。
本研究利用大语言模型（LLMs）模拟社交实验，探索其在虚拟人格扮演中模仿人类人格的能力。提出一个端到端评估框架，包含个体层面的稳定性和可识别性分析，以及称为‘渐进人格曲线’的群体层面分析，用于检验模型在人格模拟中的真实性与一致性。方法上，对传统心理测量学方法（如验证性因子分析CFA和建构效度）进行重要改进，以适应当前低水平模拟阶段的性能提升趋势，避免过早否定或方法错配。主要贡献包括：提出系统性评估框架；实证证明角色细节对人格模拟质量的关键作用；揭示角色档案的边际效用规律，尤其发现大模型人格模拟中的规模定律，为社会科学研究中应用大语言模型提供操作性评估指标与理论基础。
原文摘要 · Abstract (English)
This research focuses on using large language models (LLMs) to simulate social experiments, exploring their ability to emulate human personality in virtual persona role-playing. The research develops an end-to-end evaluation framework, including individual-level analysis of stability and identifiability, as well as population-level analysis called progressive personality curves to examine the veracity and consistency of LLMs in simulating human personality. Methodologically, this research proposes important modifications to traditional psychometric approaches (CFA and construct validity) which are unable to capture improvement trends in LLMs at their current low-level simulation, potentially leading to remature rejection or methodological misalignment. The main contributions of this research are: proposing a systematic framework for LLM virtual personality evaluation; empirically demonstrating the critical role of persona detail in personality simulation quality; and identifying marginal utility effects of persona profiles, especially a Scaling Law in LLM personality simulation, offering operational evaluation metrics and a theoretical foundation for applying large language models in social science experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。