LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

Understand

Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.

• Such models are typically outcome-specific, however, requiring training data for each target outcome, limiting their applicability to new domains.
• We test whether large language models (LLMs) can relax these requirements by using self-report data to build attitudinal and behavioral simulations, or "generative agents," that can predict responses across outcomes without outcome-specific training data.
• Using data from a diverse national sample of 1,052 Americans, we built agents from (i) two-hour, semi-structured interviews elicited using the American Voices Project interview schedule, (ii) structured surveys including General Social Survey items and the Big Five personality inventory, or (iii) both sources combined.