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            <title>Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)</title>
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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.</abstract>
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%0 Conference Proceedings
%T M-MiniGPT4: Multilingual VLLM Alignment via Translated Data
%A Han, Seung Hun Eddie
%A Mohamed, Youssef
%A Elhoseiny, Mohamed
%Y Chimoto, Everlyn Asiko
%Y Lignos, Constantine
%Y Muhammad, Shamsuddeen
%Y Abdulmumin, Idris
%Y Siro, Clemencia
%Y Adelani, David Ifeoluwa
%S Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-364-7
%F han-etal-2026-minigpt4
%X 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.
%U https://aclanthology.org/2026.africanlp-main.2/
%P 11-16

Markdown (Informal)

[M-MiniGPT4: Multilingual VLLM Alignment via Translated Data](https://aclanthology.org/2026.africanlp-main.2/) (Han et al., AfricaNLP 2026)

• M-MiniGPT4: Multilingual VLLM Alignment via Translated Data (Han et al., AfricaNLP 2026)

ACL

• Seung Hun Eddie Han, Youssef Mohamed, and Mohamed Elhoseiny. 2026. M-MiniGPT4: Multilingual VLLM Alignment via Translated Data. In Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026), pages 11–16, Rabat, Morocco. Association for Computational Linguistics.