LLMs能评估人格吗？验证对话式AI用于特质分析
Can LLMs Assess Personality? Validating Conversational AI for Trait Profiling
AI总结：
本研究验证了LLM在对话中评估人格特质的可行性，发现其在部分人格维度上与传统问卷结果相当，但需进行特定特质校准。
摘要：
本研究验证了大型语言模型（LLMs）作为问卷式人格评估的动态替代方案。通过一项被试内实验（N=33），我们比较了由引导的LLM对话生成的Big Five人格分数与金标准IPIP-50问卷的结果，并同时测量了用户感知的准确性。结果显示，收敛效度中等（r=0.38-0.58），其中宜人性、开放性和神经质分数在两种方法间统计上等同。宜人性和外向性显示出显著差异，表明需要特定特质的校准。值得注意的是，参与者认为LLM生成的档案与传统问卷结果一样准确。这些发现表明，对话式AI为传统心理测量学提供了一个有前景的新方法。
英文摘要：
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.
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。
