  In this paper, we investigate what types of stereotypical information are
captured by pretrained language models. We present the first dataset comprising
stereotypical attributes of a range of social groups and propose a method to
elicit stereotypes encoded by pretrained language models in an unsupervised
fashion. Moreover, we link the emergent stereotypes to their manifestation as
basic emotions as a means to study their emotional effects in a more
generalized manner. To demonstrate how our methods can be used to analyze
emotion and stereotype shifts due to linguistic experience, we use fine-tuning
on news sources as a case study. Our experiments expose how attitudes towards
different social groups vary across models and how quickly emotions and
stereotypes can shift at the fine-tuning stage.
