Lune

EMNLP Findings2024

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

Matús Pikuliak, Stefan Oresko, Andrea Hrckova, Marián Simko

摘要

We present GEST -a new manually created dataset designed to measure genderstereotypical 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.ID Stereotype # samples Women are 1 Emotional and irrational 254 2 Gentle, kind, and submissive 215 3 Empathetic and caring 256 4 Neat and diligent 207 5 Social 200 6 Weak 197 7 Beautiful 243 Men are 8 Tough and rough 251 9 Self-confident 229 10 Professional 215 11 Rational 231 12 Providers 222 13 Leaders

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引用它的顶会 Paper2

  1. Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMsAngelina Wang, Michelle Phan, Daniel E. Ho, Sanmi KoyejoACL 2025 · 17 次引用
  2. MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological GenerationMehul Agarwal, Aditya Aggarwal, Arnav Goel, Medha Hira 等ACL 2026

相关 Paper

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  3. StereoSet: Measuring stereotypical bias in pretrained language modelsMoin Nadeem, Anna Bethke, Siva ReddyACL 2021
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