While language embeddings have been shown to have stereotyping biases, how
these biases affect downstream question answering (QA) models remains
unexplored. We present UNQOVER, a general framework to probe and quantify
biases through underspecified questions. We show that a naive use of model
scores can lead to incorrect bias estimates due to two forms of reasoning
errors: positional dependence and question independence. We design a formalism
that isolates the aforementioned errors. As case studies, we use this metric to
analyze four important classes of stereotypes: gender, nationality, ethnicity,
and religion. We probe five transformer-based QA models trained on two QA
datasets, along with their underlying language models. Our broad study reveals
that (1) all these models, with and without fine-tuning, have notable
stereotyping biases in these classes; (2) larger models often have higher bias;
and (3) the effect of fine-tuning on bias varies strongly with the dataset and
the model size.

通过研究证明，几种重要的定型偏见：性别、国籍、种族和宗教，都对语言嵌入和问题回答模型产生影响。对基于 transformer 的 QA 模型进行研究，发现这些模型中都存在定型偏见，并且模型越大，偏见越明显。此外，分类器的 fine-tuning 会对这种偏见产生不同的影响。