# A Comparative Study of Large Language Models and Human Personality Traits

arXiv: https://arxiv.org/abs/2505.14845 · HTML: https://mlsanity.com/paper/2505.14845/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2025.

## Built on by (most cited first, 3 of 3)

- [PTCBENCH: Benchmarking Contextual Stability of Personality Traits in LLM Systems](https://mlsanity.com/paper/2602.00016/) (2026)
- [Style over Story: Measuring LLM Narrative Preferences via Structured Selection](https://mlsanity.com/paper/2510.02025/) (2025)
- [Narrative Landscape: Mapping Narrative Dispositions Across LLMs](https://mlsanity.com/paper/2605.08742/) (2026)

## Abstract (arXiv)

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.