mlsanityData to 7 Oct 2026

M-MiniGPT4: Multilingual VLLM Alignment via Translated Data

arXiv 2603.29467 ↗First posted 31 Mar 2026cs.CL cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 18 of its 29 references that are in the map. First posted on arXiv 31 Mar 2026.

Builds on

18 of 29 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  7. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  8. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  9. 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
  10. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  11. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  12. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,544
6 more
  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  2. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 822
  3. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 557
  4. 2021WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learningcited by 285
  5. 2021VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioningcited by 190
  6. 2023NLLB-CLIP -- train performant multilingual image retrieval model on a budgetcited by 24

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation
  2. 2023MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets
  3. 2024X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment
  4. 2024Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
  5. 2025Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
  6. 2025Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
  7. 2023TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
  8. 2023mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs