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

The paper introduces the concept of Persona Collapse, where large language models (LLMs) produce clustered, non-diverse responses in multi-agent simulations. It defines three diagnostic metrics—Coverage, Uniformity, and Complexity—to quantify this phenomenon across eight LLMs on tasks such as personality, moral reasoning, and self-introduction. Findings show that persona collapse is universal but varies by task, cannot be alleviated by sampling more personas, and that higher per-persona fidelity leads to more polarized populations, while persona-targeted fine-tuning further reduces diversity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• agents
• fine-tuning