M-MiniGPT4: Multilingual VLLM Alignment via Translated Data

• URL: http://arxiv.org/abs/2603.29467v1
• Date: Tue, 31 Mar 2026 09:13:38 GMT
• Title: M-MiniGPT4: Multilingual VLLM Alignment via Translated Data
• Abstract summary: M-MiniGPT4 exhibits strong vision-language understanding (VLU) capabilities across 11 languages.<n>We utilize a mixture of native multilingual and translated data to push the multilingual VLU performance of the MiniGPT4 architecture.<n>M-MiniGPT4 achieves 36% accuracy on the multilingual MMMU benchmark, outperforming state-of-the-art models in the same weight class.
• Score: 37.12451638190918
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models [38.626788948945844]
  We present a study of open large language models (LLMs) for multilingual machine translation (MT) across a range of languages.<n>We develop MiLMMT-46, which achieves top-tier multilingual translation performance across 46 languages.
  arXiv  Detail & Related papers  (2026-02-12T13:56:02Z)
• TowerVision: Understanding and Improving Multilinguality in Vision-Language Models [56.775118098058506]
  TowerVision is a family of open multilingual vision-language models for both image-text and video-text tasks.<n>By incorporating visual and cultural context during fine-tuning, our models surpass existing approaches.<n>To support further research, we publicly release all models, data, and training recipes.
  arXiv  Detail & Related papers  (2025-10-22T17:02:48Z)
• M-Prometheus: A Suite of Open Multilingual LLM Judges [65.63858815169924]
  We introduce M-Prometheus, a suite of open-weight LLM judges that can provide both direct assessment and pairwise comparison feedback on multilingual outputs.<n>M-Prometheus models outperform state-of-the-art open LLM judges on multilingual reward benchmarks spanning more than 20 languages, as well as on literary machine translation (MT) evaluation covering 4 language pairs.
  arXiv  Detail & Related papers  (2025-04-07T11:37:26Z)
• Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study [13.409987421121405]
  GemmaX2-28 is a 9B model achieving top-tier multilingual translation performance across 28 languages.<n>GemmaX2-28 consistently outperforms the state-of-the-art (SOTA) models such as TowerInstruct and XALMA.
  arXiv  Detail & Related papers  (2025-02-04T16:57:03Z)
• LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models [89.13128402847943]
  We present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision.<n>LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks.<n>We introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages.
  arXiv  Detail & Related papers  (2025-01-01T15:43:07Z)
• M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks [10.677274746850554]
  M5 is the first comprehensive benchmark designed to evaluate LMMs on diverse vision-modal tasks within a multilingual context. We highlight substantial task-agnostic performance disparities between high- and low-resource languages. We show that larger models do not necessarily outperform smaller ones in a multilingual setting.
  arXiv  Detail & Related papers  (2024-07-04T09:55:04Z)
• M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models [27.18427414844769]
  We introduce M4U, a novel benchmark for assessing the capability of multi-discipline multilingual multimodal understanding and reasoning.<n>M4U contains 10k samples covering 64 disciplines across 16 subfields in Science, Engineering, and Healthcare in six languages.<n>Using M4U, we conduct extensive evaluations of leading Large Multimodal Models (LMMs) and Large Language Models (LLMs) with external tools.
  arXiv  Detail & Related papers  (2024-05-24T15:25:28Z)
• PolyLM: An Open Source Polyglot Large Language Model [57.64420154135178]
  We present PolyLM, a multilingual large language model (LLMs) trained on 640 billion (B) tokens, avaliable in two model sizes: 1.7B and 13B. To enhance its multilingual capabilities, we 1) integrate bilingual data into training data; and 2) adopt a curriculum learning strategy that increases the proportion of non-English data from 30% in the first stage to 60% in the final stage during pre-training. Further, we propose a multilingual self-instruct method which automatically generates 132.7K diverse multilingual instructions for model fine-tuning.
  arXiv  Detail & Related papers  (2023-07-12T09:00:37Z)
• Multilingual Multimodal Learning with Machine Translated Text [27.7207234512674]
  We investigate whether machine translating English multimodal data can be an effective proxy for the lack of readily available multilingual data. We propose two metrics for automatically removing such translations from the resulting datasets. In experiments on five tasks across 20 languages in the IGLUE benchmark, we show that translated data can provide a useful signal for multilingual multimodal learning.
  arXiv  Detail & Related papers  (2022-10-24T11:41:20Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• Multilingual Translation with Extensible Multilingual Pretraining and Finetuning [77.33262578776291]
  Previous work has demonstrated that machine translation systems can be created by finetuning on bitext. We show that multilingual translation models can be created through multilingual finetuning. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance.
  arXiv  Detail & Related papers  (2020-08-02T05:36:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.