Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025)

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.

## introduction language structure fundamentally shapes human cognition, affecting how we perceive and categorize the world @xcite @xcite . languages differ vastly in their treatment of gender: "gendered" languages top: feminine-gendered "guard" (une sentinelle / die wache) yields more feminine imagery than english. bottom: masculine-gendered "gossip" (un commérage / der tratsch) produces more masculine visuals than english, illustrating how language structure influences visual representation. psycholinguistic research demonstrates that these distinctions shape cognition, causing speakers of gendered languages to attribute masculine or feminine qualities to objects based solely on their grammatical gender @xcite . as ai-generated content becomes increasingly prevalent, projected to constitute most online content by 2025 @xcite ), text-to-image (t2i) models like dall-e 3 (openai, 2024) are transforming how visual media is created. these systems convert natural language prompts directly into synthetic images, but inherit biases from their training data @xcite @xcite . although current research extensively documents demographic and stereotypical biases in t2i systems @xcite @xcite , a fundamental question remains unexplored. does grammatical gender itself, a structural feature of language rather than content, influence visual representation in ai-generated images? recent work by @xcite demonstrated that multilingual large language models (llms) reflect psycholinguistic gender associations, describing feminine nouns such as die brücke "bridge" as "beautiful" and masculine gender equivalents such as el puente as "strong." however, whether and how grammatical gender shapes visual outputs in t2i systems remains unknown. current t2i bias studies examine demographic disparities @xcite but fail to isolate the specific influence of language structure on visual representation. to address this critical gap, we investigate three research questions: rq1: does grammatical gender systematically influence gender presentation in t2i-generated images? rq2: does this effect vary between high-and medium-resource languages? rq3: how consistently does this phenomenon manifest itself in various t2i models? we introduce gramvis, the first cross-linguistic benchmark designed to isolate how grammatical gender influences visual representation in t2i systems. our dataset comprises gender-divergent words, terms where grammatical gender differs from the stereotypical gender association of the concept, from five gendered languages (french, spanish, italian, russian, german) and two genderneutral control languages (english, chinese). this design allows us to isolate the specific influence of grammatical gender while controlling for semantic content (figure 2 ). our approach differs significantly from @xcite , who studied how grammatical gender influences llm descriptions of inanimate objects (e.g., feminine "die brücke" as "beautiful"). in contrast, our work explicitly targets gender-divergent words representing human concepts across occupations, personal traits, power dynamics, relationship descriptions, and social status. a comparison of our work with related works is given in we evaluate three state-of-the-art t2i systems-dall•e 3 (openai, 2024), ideogram v3 @xcite , and flux pro 1.1 @xcite , generating 28,800 images for systematic analysis. our results reveal that grammatical gender substantially influences visual representation. masculine grammatical markers increase male representation to 73% on average (compared to 22% with gender-neutral english), while feminine grammatical markers show more variable effects, increasing female representation to 38% (compared to 28% in english). these effects are consistently stronger in high-resource languages and vary by model architecture, with flux showing the greatest sensitivity to grammatical gender. our contributions include: (i) demonstrating that language structure, independent of content, significantly shapes visual representation in t2i outputs; while most bias research has focused on english @xcite , emerging cross-linguistic studies reveal important variations. sheng et al. (2021) showed bias manifests differently across socio-linguistic contexts, while kirk et al. (2021) found variations related to grammatical gender across 12 languages, finding variations related to the grammatical gender. more recently, cao et al. (2023) established "stereotype leakage" across languages, and zhao et al. (2024) evaluated gpt models across six languages, showing persistent gender stereotypes despite language variations @xcite . our work build on this to explore how grammatical gender influences visual representation across languages. ## gramvis to investigate how grammatical gender influences visual representation, we developed gramvis, a §3.2 1 word sampling ## results our analysis reveals clear patterns in how grammatical gender influences visual representation across languages and text-to-image models. we present findings organized by our three research questions. ## conclusion our work demonstrates that grammatical gender significantly shapes visual representation in textto-image models. masculine grammatical markers consistently increase male representation compared to gender-neutral languages, while feminine markers show variable effects. these influences are stronger in high-resource languages and vary systematically by model, with flux showing high-est sensitivity to grammatical gender. these findings establish that language structure-not just content-shapes ai-generated outputs, offering new insights for assessing and mitigating bias in multilingual, multimodal systems.