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arXiv · 2603.29467

M-MiniGPT4: Multilingual VLLM Alignment via Translated Data

Seung Hun Han·Youssef Mohamed·Mohamed Elhoseiny
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Abstract

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.

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Seung Hun Han, Youssef Mohamed, Mohamed Elhoseiny. 2026-03-31. M-MiniGPT4: Multilingual VLLM Alignment via Translated Data. https://doi.org/10.18653/v1%2F2026.africanlp-main.2

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