Multi-Agent Multimodal Models for Multicultural Text to Image Generation
This addresses the issue of Western-centric bias in multimodal AI for researchers and developers, though it is incremental as it builds on existing multi-agent and LLM methods.
The paper tackles the problem of limited cross-cultural effectiveness in text-to-image generation by introducing a multi-agent framework with distinct cultural personas, resulting in improved performance over no-agent models across multiple evaluation metrics.
Large Language Models (LLMs) demonstrate impressive performance across various multimodal 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 dataset and models are available at https://github.com/OanaIgnat/MosAIG.