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

• URL: http://arxiv.org/abs/2511.17282v1
• Date: Fri, 21 Nov 2025 14:40:50 GMT
• Title: Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
• Authors: 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,
• Abstract summary: We show that current T2I models often produce culturally neutral or English-biased results under multilingual prompts.<n>We propose a probing method that localizes culture-sensitive signals to a small set of neurons in a few fixed layers.
• Score: 43.352493955825736
• License: http://creativecommons.org/licenses/by/4.0/
• 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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