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    <abstract>Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does grammatical gender influence visual representation across languages? We introduce a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations (e.g., “une sentinelle” - grammatically feminine in French but referring to the stereotypically masculine concept “guard”). Our dataset spans five gendered languages (French, Spanish, German, Italian, Russian) and two gender-neutral control languages (English, Chinese), comprising 800 unique prompts that generated 28,800 images across three state-of-the-art T2I models. Our analysis reveals that grammatical gender dramatically influences image generation: masculine grammatical markers increase male representation to 73% on average (compared to 22% with gender-neutral English), while feminine grammatical markers increase female representation to 38% (compared to 28% in English). These effects vary systematically by language resource availability and model architecture, with high-resource languages showing stronger effects. Our findings establish that language structure itself, not just content, shapes AI-generated visual outputs, introducing a new dimension for understanding bias and fairness in multilingual, multimodal systems.</abstract>
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%0 Conference Proceedings
%T Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
%A Saeed, Muhammed
%A Raza, Shaina
%A Vayani, Ashmal
%A Abdul-Mageed, Muhammad
%A Emami, Ali
%A Shehata, Shady
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F saeed-etal-2025-beyond
%X Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does grammatical gender influence visual representation across languages? We introduce a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations (e.g., “une sentinelle” - grammatically feminine in French but referring to the stereotypically masculine concept “guard”). Our dataset spans five gendered languages (French, Spanish, German, Italian, Russian) and two gender-neutral control languages (English, Chinese), comprising 800 unique prompts that generated 28,800 images across three state-of-the-art T2I models. Our analysis reveals that grammatical gender dramatically influences image generation: masculine grammatical markers increase male representation to 73% on average (compared to 22% with gender-neutral English), while feminine grammatical markers increase female representation to 38% (compared to 28% in English). These effects vary systematically by language resource availability and model architecture, with high-resource languages showing stronger effects. Our findings establish that language structure itself, not just content, shapes AI-generated visual outputs, introducing a new dimension for understanding bias and fairness in multilingual, multimodal systems.
%U https://aclanthology.org/2025.findings-emnlp.1343/
%P 24673-24695

Markdown (Informal)

[Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models](https://aclanthology.org/2025.findings-emnlp.1343/) (Saeed et al., Findings 2025)

• Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (Saeed et al., Findings 2025)

ACL

• Muhammed Saeed, Shaina Raza, Ashmal Vayani, Muhammad Abdul-Mageed, Ali Emami, and Shady Shehata. 2025. Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 24673–24695, Suzhou, China. Association for Computational Linguistics.