A Comparative Study of Large Language Models and Human Personality Traits
Journal:
arXiv
Published Date:
May 1, 2025
Abstract
Large Language Models (LLMs) have demonstrated human-like capabilities in
language comprehension and generation, becoming active participants in social
and cognitive domains. This study investigates whether LLMs exhibit
personality-like traits and how these traits compare with human personality,
focusing on the applicability of conventional personality assessment tools. A
behavior-based approach was used across three empirical studies. Study 1
examined test-retest stability and found that LLMs show higher variability and
are more input-sensitive than humans, lacking long-term stability. Based on
this, we propose the Distributed Personality Framework, conceptualizing LLM
traits as dynamic and input-driven. Study 2 analyzed cross-variant consistency
in personality measures and found LLMs' responses were highly sensitive to item
wording, showing low internal consistency compared to humans. Study 3 explored
personality retention during role-playing, showing LLM traits are shaped by
prompt and parameter settings. These findings suggest that LLMs express fluid,
externally dependent personality patterns, offering insights for constructing
LLM-specific personality frameworks and advancing human-AI interaction. This
work contributes to responsible AI development and extends the boundaries of
personality psychology in the age of intelligent systems.
