大模型在角色模拟中易出现性格趋同，导致群体缺乏多样性。
The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models
- 提出三维度评估框架：覆盖度、均匀性、复杂性，量化角色多样性
- 发现模型在不同维度上表现不一，同一模型在人格与道德领域差异显著
- 高个体还原度反而导致刻板化，适合多智能体仿真研究者关注
基于大语言模型（LLM）的应用，如多智能体模拟，需要智能体间具备群体多样性。我们识别出一种普遍存在的失效模式—— {Persona Collapse}：即使为每个智能体分配了不同角色，它们仍会收敛至少数行为模式，形成同质化群体。为量化该现象，我们提出一个框架，从三个维度衡量：角色空间的覆盖度（Coverage）、分布的均匀性（Uniformity），以及行为模式的丰富性（Complexity）。在人格模拟（BFI-44）、道德推理和自我介绍任务上评估十种大模型，发现角色坍塌存在于两个轴线上：(1) 维度层面——模型在一个维度上看似多样，但在另一维度上结构退化；(2) 领域层面——同一模型在人格模拟中坍塌最严重，却在道德推理中最为多样。此外，项目级诊断显示，行为差异仅对应粗粒度人口统计刻板印象，而非精细的个体特征。反直觉的是，实现最高单角色保真度的模型，反而产生最刻板化的群体。我们公开工具包与数据集，支持对大模型群体表现的评估。
原文摘要 · Abstract (English)
Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive failure mode we term \emph{Persona Collapse}: agents each assigned a distinct profile nonetheless converge into a narrow behavioral mode, producing a homogeneous simulated population. To quantify persona collapse, we propose a framework that measures how much of the persona space a population occupies (Coverage), how evenly agents spread across it (Uniformity), and how rich the resulting behavioral patterns are (Complexity). Evaluating ten LLMs on personality simulation (BFI-44), moral reasoning, and self-introduction, we observe persona collapse along two axes: (1) Dimensions: a model can appear diverse on one axis yet structurally degenerate on another, and (2) Domains: the same model may collapse the most in personality yet be the most diverse in moral reasoning. Furthermore, item-level diagnostics reveal that behavioral variation tracks coarse demographic stereotypes rather than the fine-grained individual differences specified in each persona. Counter-intuitively, \textbf{the models achieving the highest per-persona fidelity consistently produce the most stereotyped populations}. We release our toolkit and data to support population-level evaluation of LLMs.
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