Scaling Cultural Resources for Improving Generative Models

Hayk Stepanyan; Aishwarya Verma; Andrew Zaldivar; Rutledge Chin Feman; Erin MacMurray van Liemt; Charu Kalia; Vinodkumar Prabhakaran; Sunipa Dev

arXiv · 2510.25167

Scaling Cultural Resources for Improving Generative Models

Hayk Stepanyan·Aishwarya Verma·Andrew Zaldivar·Rutledge Chin Feman·Erin MacMurray van Liemt·Charu Kalia·Vinodkumar Prabhakaran·Sunipa Dev

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Abstract

Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been commonly 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 repeatable, scalable, 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.

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Hayk Stepanyan, Aishwarya Verma, Andrew Zaldivar, Rutledge Chin Feman, Erin MacMurray van Liemt, Charu Kalia, Vinodkumar Prabhakaran, Sunipa Dev. 2025-10-29. Scaling Cultural Resources for Improving Generative Models. https://arxiv.org/abs/2510.25167

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