使用零射击生成AI评分对简短的开放式文本进行个性评估
Aidan G C Wright1,2, Whitney R Ringwald3, Colin E Vize4
1Department of Psychology, University of Michigan, Ann Arbor, MI, USA. aidangcw@umich.edu.
Nature human behaviour
|January 30, 2026
概括
生成型大语言模型 (LLM) 现在可以从定性数据中评估人格特征,匹配或超过传统基准. 这种可访问的方法为日常生活中的个性表达提供了新的见解.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 传统的人格评估使用心理测量尺度,这可能过于简化了人格.
- 生成型大语言模型 (LLM) 为分析定性数据提供了一种新的方法.
研究的目的:
- 探索商业生成LLM的使用,以从定性叙述中评估人格特征.
- 评估基于LLM的人格分数的趋同和预测有效性.
主要方法:
- 利用七个商业生成的LLM,从自发的思维流和日常视频日记中得分五大个性特征.
- 将LLM衍生的分数与自我报告措施和既定的基准进行了比较.
- 评估了LLM生成的日常行为和心理健康结果得分的预测有效性.
主要成果:
- 基于LLM的人格评估显示与自我报告措施的趋同,与现有基准可比或超过.
- 在多个LLM中平均得分与自我报告达成最强的协议.
- 由LLM生成的特征得分预测了日常行为和心理健康结果.
结论:
- 生成型的LLM可以通过使用定性数据来提供人格评估的定量严格性.
- 这种基于LLM的方法可以在没有专业培训的情况下使用,并突出了语言叙事的价值.
- 调查结果鼓励采用LLMs进行心理评估,并强调人格表达的无处不在.
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