Text-To-Image (TTI) models, exemplified by DALL-E and StableDiffusion, have
recently gained prominence for their remarkable zero-shot capabilities in
generating images guided by textual prompts. Language, as a conduit of culture,
plays a pivotal role in these models' multilingual capabilities, which in turn
shape their cultural agency. In this study, we explore the cultural perception
embedded in TTI models by characterizing culture across three hierarchical
tiers: cultural dimensions, cultural domains, and cultural concepts. We propose
a comprehensive suite of evaluation techniques, including intrinsic evaluations
using the CLIP space, extrinsic evaluations with a Visual-Question-Answer (VQA)
model, and human assessments, to discern TTI cultural perceptions. To
facilitate our research, we introduce the CulText2I dataset, derived from four
diverse TTI models and spanning ten languages. Our experiments reveal insights
into these models' cultural awareness, cultural distinctions, and the unlocking
of cultural features, releasing the potential for cross-cultural applications.

通过对 CulText2I 数据集进行内在评估、外在评估和人类评估，探索 TTI 模型中嵌入的文化知觉，揭示了模型的文化意识、文化差异和跨文化应用的潜力。