This paper presents a Multilingual Vision Large Language Model, named M-MiniGPT4. Our model exhibits strong vision-language understanding (VLU) capabilities across 11 languages. We utilize a mixture of native multilingual and translated data to push the multilingual VLU performance of the MiniGPT4 architecture. In addition, we propose a multilingual alignment training stage that uses parallel text corpora to further enhance the multilingual capabilities of our model. M-MiniGPT4 achieves 36% accuracy on the multilingual MMMU benchmark, outperforming state-of-the-art models in the same weight class, including foundation models released after the majority of this work was completed. We open-source our models, code, and translated datasets to facilitate future research in low-resource and multilingual settings.
@article{arxiv.2603.29467,
title = {M-MiniGPT4: Multilingual VLLM Alignment via Translated Data},
author = {Seung Hun Han and Youssef Mohamed and Mohamed Elhoseiny},
journal= {arXiv preprint arXiv:2603.29467},
year = {2026}
}
Comments
6 pages, ACL 2026, Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)