@inproceedings{saeed-etal-2025-beyond,
    title = "Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models",
    author = "Saeed, Muhammed  and
      Raza, Shaina  and
      Vayani, Ashmal  and
      Abdul-Mageed, Muhammad  and
      Emami, Ali  and
      Shehata, Shady",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1343/",
    doi = "10.18653/v1/2025.findings-emnlp.1343",
    pages = "24673--24695",
    ISBN = "979-8-89176-335-7",
    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."
}