Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling (2024)

Abstract: We present GEST – a new manually created dataset designed to measure gender-stereotypical reasoning in language models and machine translation systems. GEST contains samples for 16 gender stereotypes about men and women (e.g., Women are beautiful, Men are leaders) that are compatible with the English language and 9 Slavic languages. The definition of said stereotypes was informed by gender experts. We used GEST to evaluate English and Slavic masked LMs, English generative LMs, and machine translation systems. We discovered significant and consistent amounts of gender-stereotypical reasoning in almost all the evaluated models and languages. Our experiments confirm the previously postulated hypothesis that the larger the model, the more stereotypical it usually is.

## introduction the presence of gender biases and gender stereotypes in nlp systems is an established fact @xcite . nlp systems have shown themselves to be susceptible to learning all kinds of harmful behavior. it is critical to understand what exactly is being learned by these systems and how it can influence their users. while various evaluation datasets for genderstereotypical reasoning exist ( §2), the way they interact with the concept of gender stereotype often suffers from various conceptualization pitfalls @xcite . one issue is that the concept is often reduced to overly specific phenomena, which might not generalize beyond their narrow definitions. for instance, measuring correlations between occupations and gender-coded pronouns is a popular methodology @xcite . although this approach measures a gender stereotype, it offers only limited insight into stereotypes that are not occupation-based. ## gest dataset we created the gest dataset in two phases: first, we defined 16 gender stereotypes we want to study. second, we collected and validated samples for each of these stereotypes. ## conclusion as nlp systems are becoming more ubiquitous, it is important to have appropriate models of their behavior. if we are to understand the stereotypes in these models, we need to have them properly defined. in our work, we rely on definitions of gender stereotypes that are intuitive and based on existing sociological and gender research. as we have shown, such definitions can yield a dataset that is robust, and that managed to uncover how sensitive models are towards specific gender-stereotypical ideas. we hope that this will inspire others to interact with stereotypes and even other aspects of nlp models in a way that is more grounded and transparent. our results show a pretty bleak picture of the state of the field today. different types of nlp systems have seemingly very similar patterns of behavior, indicating that they all might have learned from similar poisoned sources. at the same time, as we now have a more fine-grained view of their behavior, we can try and focus on specific issues, e.g., how to stop models from sexualizing women. this is more manageable compared to when gender bias is conceptualized as one vast and nebulous problem.