Item-Mean Surrogates: Why Richer Persona Data Fail to Improve LLMs as Human Surrogates
LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for specific individuals. We test this premise across four datasets covering more than 400,000 participants and more than 6,000 survey items and experimental outcomes. LLMs perform well at the aggregate level: their average responses closely align with average human responses to the same items. But this success largely reflects predicting each item's average human response. Once each item's human mean is removed, LLM predictions explain only 3.05% of the remaining respondent-specific variation, far below the 53.6% human test-retest benchmark. Riche...
| Authors | Daehwan Ahn, Chengfeng Mao, Dokyun Lee |
|---|---|
| Primary category | cs.CL |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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