Item-Mean Surrogates: Why Richer Personas Fail to Deliver Individual Fidelity in LLMs as Human Surrogates

The study investigates whether large language models (LLMs) can accurately simulate individual human responses by testing the assumption that richer personas improve prediction fidelity. Across four public datasets, researchers found that LLMs largely predict item means but fail to capture individual deviations, particularly the stable, person‑by‑item signal that accounts for most variance. Even with fine‑tuning and varied models, LLMs recover only a small fraction of this nuanced signal, highlighting limitations in using them as precise human surrogates.

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• llms
• fine-tuning