用对话代替问卷，大模型可有效评估人格特质
Can LLMs Assess Personality? Validating Conversational AI for Trait Profiling
- 通过引导式对话获取性格数据，与传统问卷对比验证
- 尽责性、开放性和神经质得分一致，外向性和宜人性有差异
- 用户认为大模型评估结果与传统方式一样准确
本研究验证了大型语言模型（LLMs）作为问卷法人格评估的动态替代方案。在一项被试内实验（N=33）中，我们比较了基于引导式LLM对话得出的五大性格维度评分与金标准IPIP-50问卷的结果，并测量了用户对评估准确性的感知。结果显示中等程度的一致性（r=0.38–0.58），尽责性、开放性和神经质得分在两种方法间无统计学差异；宜人性和外向性则存在显著差异，表明需进行特质特异性校准。值得注意的是，参与者对LLM生成的性格画像的准确性评价与传统问卷相当。这些发现表明，对话式AI为传统心理测量提供了一种有前景的新路径。
原文摘要 · Abstract (English)
This study validates Large Language Models (LLMs) as a dynamic alternative to questionnaire-based personality assessment. Using a within-subjects experiment (N=33), we compared Big Five personality scores derived from guided LLM conversations against the gold-standard IPIP-50 questionnaire, while also measuring user-perceived accuracy. Results indicate moderate convergent validity (r=0.38-0.58), with Conscientiousness, Openness, and Neuroticism scores statistically equivalent between methods. Agreeableness and Extraversion showed significant differences, suggesting trait-specific calibration is needed. Notably, participants rated LLM-generated profiles as equally accurate as traditional questionnaire results. These findings suggest conversational AI offers a promising new approach to traditional psychometrics.
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