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Recent studies have demonstrated how to assess the stereotypical bias inpre-trained English language models. In this work, we extend this branch ofresearch in multiple different dimensions by systematically investigating (a)mono- and multilingual models of (b) different underlying architectures withrespect to their bias in (c) multiple different languages. To that end, we makeuse of the English StereoSet data set (Nadeem et al., 2021), which wesemi-automatically translate into German, French, Spanish, and Turkish. We findthat it is of major importance to conduct this type of analysis in amultilingual setting, as our experiments show a much more nuanced picture aswell as notable differences from the English-only analysis. The main takeawaysfrom our analysis are that mGPT-2 (partly) shows surprising anti-stereotypicalbehavior across languages, English (monolingual) models exhibit the strongestbias, and the stereotypes reflected in the data set are least present inTurkish models. Finally, we release our codebase alongside the translated datasets and practical guidelines for the semi-automatic translation to encourage afurther extension of our work to other languages.

How Different Is Stereotypical Bias Across Languages?