语言语法性别会显著影响图文模型的视觉生成结果。
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
- 通过跨语言对比实验，研究语法性别如何塑造图像生成。
- 语法男性标记使男性形象出现率升至73%，女性标记达38%。
- 揭示了语言结构本身是影响多语言生成公平性的新因素。
图文生成模型中的偏见研究主要聚焦于人口统计特征和刻板印象，却忽略了语言语法性别对视觉表征的影响。本文构建了一个跨语言基准，考察语法性别与刻板性别认知不一致的词汇（如法语中语法阴性但指代‘守卫’这一典型男性概念的‘une sentinelle’）。数据集涵盖五种有语法性别语言（法语、西班牙语、德语、意大利语、俄语）和两种无性别控制语言（英语、中文），共800个独特提示，生成28,800张图像，覆盖三种主流T2I模型。分析显示，语法性别显著影响生成结果：语法阳性标记使男性形象平均占比达73%（英语为22%），语法阴性标记使女性形象占38%（英语为28%）。该效应随语言资源丰富度和模型架构系统性变化，高资源语言中表现更明显。研究证明，语言结构本身而非仅内容，塑造了多语言多模态系统的生成输出，为理解偏见与公平性提供了新维度。
原文摘要 · Abstract (English)
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.
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