Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models

Yang Trista Cao, Anna Sotnikova, Hal Daum'e, Rachel Rudinger, L. Zou
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引用次数: 14

Abstract

NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.
基于理论的美国社会刻板印象在英语语言模型中的测量
在文本上训练的NLP模型已被证明会再现人类的刻板印象,当系统大规模部署时,这可能会放大对边缘群体的伤害。我们采用社会心理学的Koch等人(2016)的代理-信念-交流(ABC)刻板印象模型作为系统研究和发现语言模型(lm)中刻板印象群体-特质关联的框架。我们引入敏感性测试(SeT)来测量语言模型的刻板印象关联。为了使用ABC模型评估SeT和其他措施,我们收集了来自美国的受试者的群体特征判断,并与英语LM刻板印象进行了比较。最后,我们将这个框架扩展到测量交叉身份的LM刻板印象。
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