Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation

Chuancheng Shi; Shangze Li; Shiming Guo; Simiao Xie; Wenhua Wu; Jingtong Dou; Chao Wu; Canran Xiao; Cong Wang; Zifeng Cheng; Fei Shen; Tat-Seng Chua

arXiv · 2511.17282

Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation

Chuancheng Shi·Shangze Li·Shiming Guo·Simiao Xie·Wenhua Wu·Jingtong Dou·Chao Wu·Canran Xiao·Cong Wang·Zifeng Cheng·Fei Shen·Tat-Seng Chua

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Abstract

Multilingual text-to-image (T2I) models have advanced rapidly in terms of visual realism and semantic alignment, and are now widely utilized. Yet outputs vary across cultural contexts: because language carries cultural connotations, images synthesized from multilingual prompts should preserve cross-lingual cultural consistency. We conduct a comprehensive analysis showing that current T2I models often produce culturally neutral or English-biased results under multilingual prompts. Analyses of two representative models indicate that the issue stems not from missing cultural knowledge but from insufficient activation of culture-related representations. We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers. Guided by this finding, we introduce two complementary alignment strategies: (1) inference-time cultural activation that amplifies the identified neurons without backbone fine-tuned; and (2) layer-targeted cultural enhancement that updates only culturally relevant layers. Experiments on our CultureBench demonstrate consistent improvements over strong baselines in cultural consistency while preserving fidelity and diversity.

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Chuancheng Shi, Shangze Li, Shiming Guo, Simiao Xie, Wenhua Wu, Jingtong Dou, Chao Wu, Canran Xiao, Cong Wang, Zifeng Cheng, Fei Shen, Tat-Seng Chua. 2025-11-21. Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation. https://arxiv.org/abs/2511.17282

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