用大型语言模型模拟人类福祉:在64个国家的64000名个人中进行了系统的验证和误估
Pat Pataranutaporn1, Nattavudh Powdthavee2, Chayapatr Archiwaranguprok1
1Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307.
概括
大型语言模型 (LLM) 不能准确地预测全球主观幸福感. 它们表现出偏见,特别是在代表性不足的地区,并且未能捕捉到文化深度,这使得它们不适合替代人类自我报告.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能伦理学 人工智能伦理学
背景情况:
- 主观福祉对于经济,医疗和政策决策至关重要.
- 准确地测量幸福感对于做出明智决策至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 在预测全球主观福祉方面的有效性.
- 将LLM的绩效与传统的统计模型和人类自我报告进行比较.
主要方法:
- 在64个国家利用了64000个人的自然语言个人资料.
- 将四个领先的LLM与自我报告的幸福感和回归模型的预测进行了比较.
- 进行了预先注册的实验,以评估LLM依赖语言协会与概念理解的依赖.
主要成果:
- 与回归模型相比,LLM产生了可信的模式,但系统性地表现不佳.
- 在代表性不足的国家,LLM错误最大,反映了数字和经济不平等.
- 法律法学表明依赖于表面层面的语言关联,导致在不熟悉的背景下可预测的扭曲.
结论:
- 法律学士可以模拟生活满意度的广泛相关性,但缺乏经验和文化深度.
- 简单的生活方式并不能替代人类对幸福感的自我报告.
- 使用LLM进行福利评估可能会加剧不平等,并削弱人类的行动能力.
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