On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation
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Computer Science > Computation and Language
Title:On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation
Abstract:Text-to-image (T2I) generation has achieved remarkable progress in recent years. However, existing research has largely focused on English-only settings, leaving cross-lingual performance gaps and language-specific effects insufficiently explored. To fill this gap, we introduce LingT2I, a benchmark covering 10 widely used languages with 33K prompts, designed to evaluate cross-lingual effects in both content generation and text rendering. Building on this benchmark, we conduct a comprehensive cross-lingual analysis, uncovering linguistic inequality and language-dependent trade-offs across evaluation dimensions. Beyond quantitative evaluation, we further reveal a range of language-dependent generation patterns, highlighting how linguistic factors and their corresponding cultural contexts systematically impact model outputs. Our benchmark and analysis provide a foundation for studying cross-lingual behavior in T2I generation and facilitate the development of more robust and inclusive models. Code and dataset are available at this https URL.
| Comments: | Accepted to ACM MM 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.11002 [cs.CL] |
| (or arXiv:2608.11002v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11002
arXiv-issued DOI via DataCite (pending registration)
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