@inproceedings{stepanyan-etal-2026-scaling,
    title = "Scaling Cultural Resources for Improving Generative Models",
    author = "Stepanyan, Hayk  and
      Verma, Aishwarya  and
      Zaldivar, Andrew  and
      Feman, Rutledge Chin  and
      van Liemt, Erin MacMurray  and
      Kalia, Charu  and
      Prabhakaran, Vinodkumar  and
      Dev, Sunipa",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.352/",
    pages = "6695--6709",
    ISBN = "979-8-89176-386-9",
    abstract = "Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been known to be conducted to improve overall model performance, bolstering and evaluating this cross-cultural competence of generative AI models requires data resources to be intentionally expanded to include global contexts and languages. In this work, we construct a multi-pronged pipeline to collect and contribute culturally salient, multilingual data. We posit that such data can assess the state of the global applicability of our models and thus, in turn, help identify and improve upon cross-cultural gaps."
}