分析大模型文本性格特征，发现其更友善、情绪更稳定
Who are you, ChatGPT? Personality and Demographic Style in LLM-Generated Content
- 用自动分类器分析模型对开放问题的回答，避免问卷依赖
- 模型表现更高亲和力、更低神经质，倾向合作稳定交流
- 适合关注AI人格化与社会影响的研究者和从业者
生成式大语言模型已深度融入日常生活，广泛生成类人文本。现有研究开始探讨这些模型是否在语言中表现出类似人格与人口统计特征。本文提出一种新数据驱动方法，不依赖自述问卷，而是利用自动人格与性别分类器分析模型在Reddit上对开放问题的回答。对比六种主流模型与人类写作，发现模型普遍表现出更高的宜人性和更低的神经质，反映其合作性与稳定的对话倾向。模型语言中的性别特征虽总体接近人类写作者，但差异较小，与先前关于自动化代理的研究结果一致。本研究贡献了一个包含人类与模型回答的新数据集，并提供了大规模比较分析，为生成式AI的人格与人口统计模式研究提供了新视角。
原文摘要 · Abstract (English)
Generative large language models (LLMs) have become central to everyday life, producing human-like text across diverse domains. A growing body of research investigates whether these models also exhibit personality- and demographic-like characteristics in their language. In this work, we introduce a novel, data-driven methodology for assessing LLM personality without relying on self-report questionnaires, applying instead automatic personality and gender classifiers to model replies on open-ended questions collected from Reddit. Comparing six widely used models to human-authored responses, we find that LLMs systematically express higher Agreeableness and lower Neuroticism, reflecting cooperative and stable conversational tendencies. Gendered language patterns in model text broadly resemble those of human writers, though with reduced variation, echoing prior findings on automated agents. We contribute a new dataset of human and model responses, along with large-scale comparative analyses, shedding new light on the topic of personality and demographic patterns of generative AI.
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