Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases. Quantifying these biases is challenging because current methods focusing on fill-the-mask objectives are sensitive to slight changes in input. To address this, we propose LABDet, a robust language-agnostic method for evaluating bias in PLMs. For nationality as a case study, we show that LABDet "surfaces" nationality bias by training a classifier on top of a frozen PLM on non-nationality sentiment detection. Collaborating with political scientists, we find consistent patterns of nationality bias across monolingual PLMs in six languages that align with historical and political context. We also show for English BERT that bias surfaced by LABDet correlates well with bias in the pretraining data; thus, our work is one of the few studies that directly links pretraining data to PLM behavior. Finally, we verify LABDet's reliability and applicability to different templates and languages through an extensive set of robustness checks.

本研究提出了一种名为LABDet的鲁棒的、语言无关的方法，用于评估PLMs中的社会偏见，且通过实验证明LABDet能展现国籍偏见，我们在六种语言上使用该方法，并验证了其可靠性和适用性。

语言模型中的语言无关偏见检测