Scaling Cultural Resources for Improving Generative Models

TL;DR

This paper presents a scalable pipeline for collecting multilingual, culturally relevant data to enhance and evaluate the global cultural competence of generative AI models.

Contribution

It introduces a repeatable, scalable method for expanding cultural resources, addressing cross-cultural gaps in generative models.

Findings

Developed a pipeline for multilingual data collection

Enables assessment of global applicability of models

Supports targeted improvements in cultural competence

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.