Date of Award
6-9-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Oana Ignat
Abstract
Large Language Models (LLMs) demonstrate impressive performance across various multimodel tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of existing data and models. Meanwhile, multi-agent models have shown strong capabilities in solving complex tasks. In this paper, we evaluate the performance of LLMs in a multi-agent interaction setting for the novel task of multicultural image generation. Our key contributions are: (1) We introduce MosAIG, a Multi-Agent framework that enhances multicultural Image Generation by leveraging LLMs with distinct cultural personas; (2) We provide a dataset of 9,000 multicultural images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages; and (3) We demonstrate that multi-agent interactions outperform simple, no-agent models across multiple evaluation metrics, offering valuable insights for future research. Our sample dataset and models are available at https://github.com/AIM-SCU/MosAIG, together with the complete dataset at https://huggingface.co/datasets/ParthGeek/Multi-Cultural-Single-Multi-Agent-Images
Recommended Citation
Bhalerao, Parth, "Multi-Agent Multimodal Models for Multicultural Text to Image Generation" (2025). Computer Science and Engineering Master's Theses. 51.
https://scholarcommons.scu.edu/cseng_mstr/51
