使用大型语言模型推断社交媒体活动的个性:跨模型协议,时间稳定性和自我报告的融合有效性
Davide Marengo1, Christian Montag2,3,4, Michele Settanni1
1Department of Psychology, University of Turin, Turin, Italy.
Journal of personality
|September 2, 2025
概括
大型语言模型 (LLM) 可以从数字足迹推断人格特征, 但可靠性和有效性需要增强. 在不同模型和时间点进行推断可以提高人格评估的准确性.
科学领域:
- 计算社会科学
- 心理评估
- 人工智能
背景情况:
- 大型语言模型 (LLM) 显示出从数字数据中推断不显眼的人格特征的潜力.
- 基于LLM的人格评估的可靠性和有效性需要进一步调查.
研究的目的:
- 评估LLM从社交媒体数据中得出的个性特征推断的可靠性和有效性.
- 为了比较Gemini 1.5 Pro和GPT-4o在人格评估中的表现.
主要方法:
- 用双子座 1.5 Pro 和 GPT-4o 来推断大五个人的性格特征, 来自 2 年的 Facebook 帖子 (N=1214 位意大利用户).
- 通过使用十项人格清单来比较LLM的预测与自我报告的人格.
主要成果:
- LLM的预测显示了变化,低估了同意和良心,高估了外向,而神经主义和开放性与自我报告保持一致.
- 在LLM和时间点之间汇总推断提高了可靠性和时间稳定性,在Extraversion中跨LLM的一致性达到0.83.
- 与自我报告的相关性很小,在结合LLM推断时,开放率最高为0.31.
结论:
- 在模型和时间点中汇总LLM推断对于提高人格评估的可靠性和有效性至关重要.
- 虽然不完美,但LLM提供了一个从数字足迹推断个性的工具.
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