大型语言模型在传输链实验中显示了类似人类的内容偏差
Alberto Acerbi1, Joseph M Stubbersfield2
1Department of Sociology and Social Research, University of Trento, Trento 38122, Italy.
概括
像ChatGPT-3这样的大型语言模型 (LLM) 显示出类似人类的偏见. 他们倾向于性别刻板印象,社会内容,消极性,威胁和在培训数据中发现的反直觉信息.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 文化进化 文化进化
背景情况:
- 人类的文化传播表现出偏见,偏爱某些类型的内容.
- 大型语言模型 (LLM) 越来越普遍,可能会继承人类的偏见.
研究的目的:
- 调查LLM,特别是ChatGPT-3,是否表现出与人类文化传播相似的偏见.
- 为了确定在LLM输出中存在的内容偏差类型.
主要方法:
- 使用传输链实验方法,反映了人类研究.
- 使用预先注册的实验材料从先前的人类参与者研究.
- 分析了ChatGPT-3输出的内容偏差.
主要成果:
- 聊天GPT-3在性别刻板印象一致的内容方面表现出偏见.
- 在LLM的结果显示,社会,负面和威胁相关信息的偏见.
- 对于其他类型的生物学上反直觉的内容也观察到偏见.
结论:
- 在培训数据中,LLM偏见表明广泛的,认知上有吸引力的内容.
- 这些偏见可能会放大人类对非信息内容的现有倾向.
- 需要进一步研究LLM偏见的下游影响.
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