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

arXiv: https://arxiv.org/abs/2311.18711 · HTML: https://mlsanity.com/paper/2311.18711/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 24 of 24)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Evaluating Gender Bias in Machine Translation](https://mlsanity.com/paper/1906.00591/) (2019)
- [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://mlsanity.com/paper/2010.06032/) (2020)
- [Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.03310/) (2019)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases](https://mlsanity.com/paper/2010.15052/) (2020)
- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations](https://mlsanity.com/paper/2203.13928/) (2022)
- [Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://mlsanity.com/paper/2101.03289/) (2021)
- [Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation](https://mlsanity.com/paper/2109.03858/) (2021)
- [On Measuring Gender Bias in Translation of Gender-neutral Pronouns](https://mlsanity.com/paper/1905.11684/) (2019)
- [Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models](https://mlsanity.com/paper/2101.09688/) (2021)
- [Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias](https://mlsanity.com/paper/2206.09860/) (2022)
- [The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks](https://mlsanity.com/paper/2210.10040/) (2022)
- [Choose Your Lenses: Flaws in Gender Bias Evaluation](https://mlsanity.com/paper/2210.11471/) (2022)
- [Evaluating Gender Bias in Hindi-English Machine Translation](https://mlsanity.com/paper/2106.08680/) (2021)
- [GATE: A Challenge Set for Gender-Ambiguous Translation Examples](https://mlsanity.com/paper/2303.03975/) (2023)
- [Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models](https://mlsanity.com/paper/2108.10379/) (2021)
- [Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives](https://mlsanity.com/paper/2107.11584/) (2021)
- [How Reliable are Model Diagnostics?](https://mlsanity.com/paper/2105.05641/) (2021)
- [In-Depth Look at Word Filling Societal Bias Measures](https://mlsanity.com/paper/2302.12640/) (2023)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)

## Built on by (most cited first, 12 of 12)

- [SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety](https://mlsanity.com/paper/2404.05399/) (2024)
- [Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs](https://mlsanity.com/paper/2502.01926/) (2025)
- [Evaluating the Effect of Retrieval Augmentation on Social Biases](https://mlsanity.com/paper/2502.17611/) (2025)
- [EuroGEST: Investigating gender stereotypes in multilingual language models](https://mlsanity.com/paper/2506.03867/) (2025)
- [RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models](https://mlsanity.com/paper/2601.03699/) (2026)
- [Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts](https://mlsanity.com/paper/2502.13640/) (2025)
- [Harsher on Male? Evaluating LLMs on Gender-Asymmetric Moral Framing Across Diverse Conflict Scenarios](https://mlsanity.com/paper/2606.14068/) (2026)
- [Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks](https://mlsanity.com/paper/2502.11611/) (2025)
- [Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory](https://mlsanity.com/paper/2505.14080/) (2025)
- [A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains](https://mlsanity.com/paper/2511.01840/) (2025)
- [MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation](https://mlsanity.com/paper/2604.18914/) (2026)
- [Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information](https://mlsanity.com/paper/2610.01696/) (2026)

## Abstract (arXiv)

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.