Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering

TL;DR

This study systematically evaluates six large language models using the Big Five framework, revealing temperature-dependent trait expressions and architectural clustering, which inform model tuning and ethical considerations.

Contribution

The paper introduces a comprehensive analysis of personality-like traits in LLMs, highlighting how temperature and architecture influence emergent personality patterns.

Findings

Neuroticism and Extraversion vary with temperature adjustments.

Distinct model clusters correlate with architectural features.

Personality-like traits emerge differently across models.

Abstract

As Large Language Models (LLMs) become integral to human-centered applications, understanding their personality-like behaviors is increasingly important for responsible development and deployment. This paper systematically evaluates six LLMs, applying the Big Five Inventory-2 (BFI-2) framework, to assess trait expressions under varying sampling temperatures. We find significant differences across four of the five personality dimensions, with Neuroticism and Extraversion susceptible to temperature adjustments. Further, hierarchical clustering reveals distinct model clusters, suggesting that architectural features may predispose certain models toward stable trait profiles. Taken together, these results offer new insights into the emergence of personality-like patterns in LLMs and provide a new perspective on model tuning, selection, and the ethical governance of AI systems. We share the data and code for this analysis here: https://osf.io/bsvzc/?view_only=6672219bede24b4e875097426dc3fac1