Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
This work provides a systematic evaluation framework for applying LLMs to social science experiments, though it is incremental in adapting psychometric methods.
This research tackled the problem of evaluating large language models (LLMs) in simulating human personality for social experiments, finding that more detailed persona profiles significantly improve simulation quality and revealing a scaling law in this context.
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.