@inproceedings{friedrich-etal-2025-multilingual,
    title = "Multilingual Text-to-Image Generation Magnifies Gender Stereotypes",
    author = {Friedrich, Felix  and
      H{\"a}mmerl, Katharina  and
      Schramowski, Patrick  and
      Brack, Manuel  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Fraser, Alexander  and
      Kersting, Kristian},
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.966/",
    pages = "19656--19679",
    ISBN = "979-8-89176-251-0",
    abstract = "Text-to-image (T2I) generation models have achieved great results in image quality, flexibility, and text alignment, leading to widespread use. Through improvements in multilingual abilities, a larger community can access this technology. Yet, we show that multilingual models suffer from substantial gender bias. Furthermore, the expectation that results should be similar across languages does not hold. We introduce MAGBIG, a controlled benchmark designed to study gender bias in multilingual T2I models, and use it to assess the impact of multilingualism on gender bias. To this end, we construct a set of multilingual prompts that offers a carefully controlled setting accounting for the complex grammatical differences influencing gender across languages. Our results show strong gender biases and notable language-specific differences across models. While we explore prompt engineering strategies to mitigate these biases, we find them largely ineffective and sometimes even detrimental to text-to-image alignment. Our analysis highlights the need for research on diverse language representations and greater control over bias in T2I models."
}