  A stereotype is an over-generalized belief about a particular group of
people, e.g., Asians are good at math or Asians are bad drivers. Such beliefs
(biases) are known to hurt target groups. Since pretrained language models are
trained on large real world data, they are known to capture stereotypical
biases. In order to assess the adverse effects of these models, it is important
to quantify the bias captured in them. Existing literature on quantifying bias
evaluates pretrained language models on a small set of artificially constructed
bias-assessing sentences. We present StereoSet, a large-scale natural dataset
in English to measure stereotypical biases in four domains: gender, profession,
race, and religion. We evaluate popular models like BERT, GPT-2, RoBERTa, and
XLNet on our dataset and show that these models exhibit strong stereotypical
biases. We also present a leaderboard with a hidden test set to track the bias
of future language models at https://stereoset.mit.edu
