The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents
Journal:
arXiv
Published Date:
Feb 28, 2025
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) How do personality traits affect
problem-solving in closed tasks? (2) How do traits shape creativity in open
tasks? (3) 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. We demonstrate that LLMs
inherently simulate human behavior through next-token prediction, mirroring
human language, decision-making, and collaborative dynamics.
