Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering
This work addresses the problem of understanding and managing personality-like behaviors in LLMs for responsible AI development and deployment, though it is incremental in applying existing psychological frameworks to AI.
The paper systematically evaluated six large language models using the Big Five Inventory-2 framework to assess personality-like traits under varying sampling temperatures, finding significant differences in four traits with Neuroticism and Extraversion being temperature-sensitive, and identified distinct model clusters based on architectural features.
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