  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.
