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Bhatia, Mehar

University of British Columbia

This thesis explores the concept of cultural competence and its role in enhancing language and multimodal models to understand and interpret human behaviour across diverse cultural contexts. In response to the increasing need for culturally inclusive models, this work addresses three major research questions by developing new methodologies, metrics, and tools. Firstly, we present GD-COMET, a geo-diverse variant of the COMET model, designed to generate culturally nuanced commonsense inferences. Secondly, we introduce GlobalRG, a benchmark tailored to evaluate the multicultural understanding of vision-language models. This benchmark highlights existing cultural biases and gaps in representation, providing a comprehensive assessment in diverse settings. Furthermore, we introduce CulturalSnap, a large-scale dataset comprising image-text pairs from 50 diverse cultures, and design an approach for inclusive representation learning by leveraging carefully designed contrastive learning objectives to improve model performance across varied cultural contexts. By addressing these critical areas, this work contributes to the development of models that are not only more aware of cultural diversity but also more adept at interacting fairly and effectively with socioculturally diverse audiences. Through these advancements, we aim to foster a more equitable and inclusive AI landscape.

Text

2024-08-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89107

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/