Personality Traits in Large Language Models: A Psychometric Evaluation

Agents4Science 2025 Conference Submission58 Authors

Keywords: Personality, Large Language Model, Machine Psychology, Psychometric Assessment, Personality-Architecture Embedding

Abstract: Large language models (LLMs) have revolutionized artificial intelligence, enabling human-like interactions that prompt inquiries into their emergent personality traits—stable patterns of behavior, cognition, and affect. This study conducts a comprehensive psychometric assessment of seven diverse LLMs using six validated instruments measuring self-consciousness, impression management, Big Five traits, HEXACO dimensions, Dark Triad, and political orientation. Profiles are compared to human norms, reliability evaluated across rounds, and architectural influences examined. LLMs exhibit amplified prosocial traits (e.g., agreeableness $d=1.22$) and moderate reliability (avg $r=0.65$, $\text{ICC}=0.68$). RLHF predicts lower psychopathy ($\beta=-0.45$). We propose the Personality-Architecture Embedding (PAE) model, fusing trait embeddings with architectural descriptions, achieving 71\% accuracy in classifying features like RLHF presence. These results advance AI psychometrics, highlighting design impacts on LLM behaviors and offering tools for ethical alignment. Data and code are available as *Supplementary Material* (attachment) to this submission, as well as at: https://anonymous.4open.science/r/Agents4Science_2025_LLM_personality-QQQQ.

Submission Number: 58