How Well Do Large Language Models Capture Human Personality?
Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks. In this work, we formalize these assumptions and systematically evaluate them across multiple architectures, scales, and simulation settings. We identify a fundamental limitation we term persona manifold collapse, where increasingly expressive persona specifications lead to systematic contraction of representational and behavioral diversity. Across models, increasing persona complexity c...
| Authors | Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah et al. |
|---|---|
| Primary category | cs.HC |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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