2025/08/05 by Muhammed Saeed, Saeed, Muhammed, Raza, Shaina +8 · 2 citations
Computer Science · Neuroscience · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language, Metaphor, and Cognition #Multimodal Machine Learning Applications #Neurobiology of Language and Bilingualism
paper · pdf · doi:10.48550/arxiv.2508.03199
openalex publication_date 2025/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.