LLM的个性像流水，随输入变化，不像人有稳定性格。
A Comparative Study of Large Language Models and Human Personality Traits
- 用行为测试法分析LLM在不同情境下的表现
- 发现LLM性格易变，对问题措辞敏感，内部一致性低
- 适合研究人机交互与负责任的AI设计
大型语言模型（LLMs）在语言理解与生成方面展现出类人能力，正积极参与社会与认知领域。本研究探究了LLM是否表现出类似人格的特质及其与人类人格的差异，重点关注传统人格评估工具的适用性。通过三个实证研究，采用行为测试法：研究1考察重测稳定性，发现LLM的变异性和输入敏感性高于人类，缺乏长期稳定性；据此提出分布式人格框架，将LLM人格视为动态、受输入驱动的特征。研究2分析跨版本一致性，发现LLM对题目措辞高度敏感，内部一致性显著低于人类。研究3探讨角色扮演中的人格保持，表明LLM人格由提示词和参数设定决定。结果表明，LLM呈现流变、外部依赖的人格模式，为构建专属于LLM的人格框架提供了依据，并推动了智能时代人格心理学的发展。该研究有助于负责任的AI开发，拓展了人格心理学边界。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。