大型语言模型可以推断到社交媒体用户的心理倾向
Heinrich Peters1, Sandra C Matz1
1Columbia Business School, Columbia University, New York, NY 10027, USA.
PNAS nexus
|July 1, 2024
概括
大型语言模型 (LLM) 可以从社交媒体文本中推断出人格特征,其准确性与专业模型相美. 然而,准确性因性别和年龄而异,表明潜在的数据偏差.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 大型语言模型 (LLM) 展示了先进的人类能力.
- 从数字足迹来评估心理倾向是一个新兴的研究领域.
研究的目的:
- 评估LLM (GPT-3.5和GPT-4) 从Facebook状态更新中推断大五个人的性格特征的准确性.
- 调查基于LLM的人格推断准确度的人口变化.
主要方法:
- 使用GPT-3.5和GPT-4的零射击学习方法.
- 对Facebook状态更新与自我报告的五大个性特征进行分析.
- 检查跨年龄和性别群体的精度差异.
主要成果:
- 在人格特征推断方面,LLM的准确性与监督机器学习模型相提并论.
- 推理准确度显示了人口群体之间的异质性,女性和年轻人更高.
- 培训数据中的潜在偏差或在线自我表达的差异可能解释了准确度的变化.
结论:
- 法律法学提供了一个可扩展的方法,用于心理测量评估,潜在的民主化研究和个性化服务.
- 关于隐私和自决的伦理考虑,需要为心理学LLM应用提供强大的监管框架.
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