Publikation
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes
Felix Friedrich; Katharina Hämmerl; Patrick Schramowski; Manuel Brack; Jindrich Libovický; Alexander Fraser; Kristian Kersting
In: Wanxiang Che; Joyce Nabende; Ekaterina Shutova; Mohammad Taher Pilehvar (Hrsg.). Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025, Vienna, Austria, July 27 - August 1, 2025. Annual Meeting of the Association for Computational Linguistics (ACL), Pages 19656-19679, Association for Computational Linguistics, 2025.
Zusammenfassung
Text-to-image (T2I) generation models have
achieved great results in image quality, flexibil-
ity, 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. Fur-
thermore, the expectation that results should be
similar across languages does not hold. We
introduce MAGBIG, a controlled benchmark de-
signed to study gender bias in multilingual T2I
models, and use it to assess the impact of multi-
lingualism on gender bias. To this end, we
construct a set of multilingual prompts that
offers a carefully controlled setting account-
ing for the complex grammatical differences
influencing gender across languages. Our re-
sults show strong gender biases and notable
language-specific differences across models.
While we explore prompt engineering strate-
gies to mitigate these biases, we find them
largely ineffective and sometimes even detri-
mental to text-to-image alignment. Our analy-
sis highlights the need for research on diverse
language representations and greater control
over bias in T2I models.1
