大语言模型与人类人格特质的比较研究
A Comparative Study of Large Language Models and Human Personality Traits
AI总结：
本研究通过三项实证实验探究大语言模型的类人格特质，发现其人格表现流动且依赖外部输入，据此提出分布式人格框架，为人机交互与负责任AI发展提供参考。
摘要：
大语言模型（Large Language Models, LLMs）已在语言理解与生成方面展现出类人能力，成为社会与认知领域的活跃参与者。本研究聚焦传统人格评估工具的适用性，探究LLMs是否表现出类人格特质，以及这些特质与人类人格的差异。研究采用基于行为的方法开展三项实证研究：研究1检验了重测稳定性，发现LLMs的变异性更高，比人类更易受输入影响，缺乏长期稳定性。基于此，我们提出分布式人格框架（Distributed Personality Framework），将LLM的特质概念化为动态且由输入驱动的。研究2分析了人格测量的跨变体一致性，发现LLMs的反应对题目措辞高度敏感，与人类相比内部一致性较低。研究3探索了角色扮演中的人格保持情况，表明LLM的特质由提示词和参数设置塑造。这些发现表明，LLMs表现出流动的、依赖外部因素的人格模式，为构建LLM专属的人格框架和推进人机交互提供了洞见。本研究助力负责任的AI发展，并拓展了智能系统时代人格心理学的研究边界。
英文摘要：
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.
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