The Power of Personality: A Human Simulation Perspective to Investigate Large Language Model Agents
This work addresses the gap in understanding LLM intelligence by connecting it to human-like personality traits, offering insights for AI simulation and collaboration, though it is incremental in applying existing psychological frameworks to LLMs.
The paper investigated how assigning Big Five personality traits to large language model (LLM) agents affects their performance in closed and open tasks, revealing that specific traits significantly influence reasoning accuracy and creative output, and that multi-agent systems show distinct collective intelligence.
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.