Multi-Agent Multimodal Models for Multicultural Text to Image Generation

ACL ARR 2025 February Submission2703 Authors

Abstract:

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://anonymous.4open.science/r/MosAIG.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: multimodal, image generation, LLM, multicultural, multilingual

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English, Hindi, German, Vietnamese, Spanish

Submission Number: 2703