The paper "StereoSet: Measuring Stereotypical Bias in Pretrained Language Models" by Moin Nadeem, Anna Bethke, and Siva Reddy addresses the issue of stereotypical biases in pretrained language models. The authors propose StereoSet, a large-scale natural English dataset designed to measure stereotypical biases in four domains: gender, profession, race, and religion. They contrast the stereotypical bias and language modeling ability of popular models such as BERT, GPT2, ROBERTA, and XLNET. The study finds that these models exhibit strong stereotypical biases. The authors also introduce Context Association Tests (CATs) to evaluate both bias and language modeling ability, addressing limitations of existing methods that focus on artificial sentences or do not consider language modeling performance. The data and code for StereoSet are available online. The paper highlights the importance of quantifying and mitigating biases in language models to ensure fairness and trustworthiness.The paper "StereoSet: Measuring Stereotypical Bias in Pretrained Language Models" by Moin Nadeem, Anna Bethke, and Siva Reddy addresses the issue of stereotypical biases in pretrained language models. The authors propose StereoSet, a large-scale natural English dataset designed to measure stereotypical biases in four domains: gender, profession, race, and religion. They contrast the stereotypical bias and language modeling ability of popular models such as BERT, GPT2, ROBERTA, and XLNET. The study finds that these models exhibit strong stereotypical biases. The authors also introduce Context Association Tests (CATs) to evaluate both bias and language modeling ability, addressing limitations of existing methods that focus on artificial sentences or do not consider language modeling performance. The data and code for StereoSet are available online. The paper highlights the importance of quantifying and mitigating biases in language models to ensure fairness and trustworthiness.