The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents

ACL ARR 2025 May Submission7064 Authors

Abstract:

Large language models (LLMs) excel in both closed tasks (including problem-solving, and code generation) and open tasks (including creative writing), yet existing explanations for their capabilities lack connections to real-world human intelligence.
To fill this gap, this paper systematically investigates LLM intelligence through the lens of ``human simulation'', addressing three core questions: (1) \textit{How do personality traits affect problem-solving in closed tasks?} (2) \textit{How do traits shape creativity in open tasks?} (3) \textit{How does single-agent performance influence multi-agent collaboration?}
By assigning Big Five personality traits to LLM agents and evaluating their performance in single- and multi-agent settings, we reveal that specific traits significantly influence reasoning accuracy (closed tasks) and creative output (open tasks).
Furthermore, multi-agent systems exhibit collective intelligence distinct from individual capabilities, driven by distinguishing combinations of personalities.

Paper Type: Long

Research Area: Human-Centered NLP

Research Area Keywords: human factors in NLP, human-centered evaluation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study

Languages Studied: English

Submission Number: 7064