The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models

Abstract

Large language models (LLMs) have powered multi-agent simulations such as survey respondents or application users. However, LLMs lack the population-level diversity required by these simulations---responses from LLMs with detailed human profiles cluster into classes, unlike human responses that uniformly fill the entire space. In this paper, we formalize this as Persona Collapse and quantify its population-level structure with three diagnostic metrics: Coverage (how much of the persona space is occupied), Uniformity (how evenly agents spread across it), and Complexity (how rich the resulting behaviors are). Across eight LLMs on personality (BFI-44), moral reasoning, and self-introduction, persona collapse is universal but task-dependent: CoSER-Llama-8B collapses the most in personality while being the most diverse in moral reasoning. Moreover, sampling more personas cannot mitigate the collapse. We further show a fidelity--diversity tradeoff: the models with the highest per-persona fidelity produce the most polarized populations. Finally, persona-targeted fine-tuning further narrows the population instead of diversifying it.