解码大语言模型中涌现的大五人格特质：温度依赖的表达与架构聚类
Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering
AI总结：
本文使用BFI-2框架评估六个大语言模型在不同采样温度下的人格特质表达，发现四个维度存在显著差异且神经质和外向性对温度敏感，并通过层次聚类揭示架构特征与稳定特质轮廓的关联，为模型调优与伦理治理提供新视角。
摘要：
随着大语言模型（LLMs）日益成为以人为中心应用的核心组成部分，理解其类人格行为对于负责任的开发与部署愈发重要。本文系统评估了六个LLMs，应用大五人格问卷第二版（BFI-2）框架，考察不同采样温度下的人格特质表达。我们发现在五个人格维度中有四个存在显著差异，其中神经质和外向性对温度调整尤为敏感。此外，层次聚类揭示了不同的模型簇，表明架构特征可能使某些模型倾向于表现出稳定的人格特质轮廓。综合来看，这些结果为LLMs中类人格模式的出现提供了新的见解，并为模型调优、选择以及人工智能系统的伦理治理提供了新的视角。我们在以下链接共享本分析的数据和代码：https://osf.io/bsvzc/?view_only=6672219bede24b4e875097426dc3fac1
英文摘要：
As Large Language Models (LLMs) become integral to human-centered applications, understanding their personality-like behaviors is increasingly important for responsible development and deployment. This paper systematically evaluates six LLMs, applying the Big Five Inventory-2 (BFI-2) framework, to assess trait expressions under varying sampling temperatures. We find significant differences across four of the five personality dimensions, with Neuroticism and Extraversion susceptible to temperature adjustments. Further, hierarchical clustering reveals distinct model clusters, suggesting that architectural features may predispose certain models toward stable trait profiles. Taken together, these results offer new insights into the emergence of personality-like patterns in LLMs and provide a new perspective on model tuning, selection, and the ethical governance of AI systems. We share the data and code for this analysis here: https://osf.io/bsvzc/?view_only=6672219bede24b4e875097426dc3fac1
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
