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AIM-SCU/When-Cultures-Meet

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Permissive or share-alike

The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 1c983be
Licence files: No licence file at the top level of the repositoryEvidence: file list at 1c983be
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

text-to-image

Citation text from the card

@inproceedings{bhalerao-etal-2026-cultures,
    title = "When Cultures Meet: Multicultural Text-to-Image Generation",
    author = "Bhalerao, Parth and Ignat, Oana and Trinh, Brian and Yalamarty, Mounika",
    editor = "Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1783/",
    doi = "10.18653/v1/2026.findings-acl.1783",
    pages = "35808--35828",
    ISBN = "979-8-89176-395-1",
    abstract = "Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultural scenes{---}where people and landmarks originate from different cultures{---}remains largely unexplored. We introduce multicultural text-to-image generation as a new task and present the first benchmark designed to study this setting. Our dataset contains 9,000 images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages. Using this benchmark, we analyze the behavior of state-of-the-art text-to-image models across multiple dimensions, including alignment, image quality, aesthetics, knowledge, and fairness. As one strategy for composing cultural and demographic information,

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dataset_licence({ "id": "AIM-SCU/When-Cultures-Meet" })
signal: "permissive"
stated_licence: "mit"
sources: 0