Multilingual Training and Evaluation Resources for Vision-Language Models

• URL: http://arxiv.org/abs/2604.18347v1
• Date: Mon, 20 Apr 2026 14:42:47 GMT
• Title: Multilingual Training and Evaluation Resources for Vision-Language Models
• Abstract summary: We introduce a new comprehensive suite of resources for Vision Language Models (VLMs) training and evaluation spanning five European languages.<n>We use a regeneration-translation paradigm that produces high-quality cross-lingual resources by combining curated synthetic generation and manual annotation.<n>Experiments show that using multilingual, multimodal examples for training VLMs aids is consistently beneficial on non-English benchmarks.
• Score: 2.5812054740604453
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years. However, despite their growth, VLMs development is heavily grounded on English, leading to two main limitations: (i) the lack of multilingual and multimodal datasets for training, and (ii) the scarcity of comprehensive evaluation benchmarks across languages. In this work, we address these gaps by introducing a new comprehensive suite of resources for VLMs training and evaluation spanning five European languages (English, French, German, Italian, and Spanish). We adopt a regeneration-translation paradigm that produces high-quality cross-lingual resources by combining curated synthetic generation and manual annotation. Specifically, we build Multi-PixMo, a training corpus obtained regenerating examples from Pixmo pre-existing datasets with permissively licensed models: PixMo-Cap, PixMo-AskModelAnything, and CoSyn-400k. On the evaluation side, we construct a set of multilingual benchmarks derived translating widely used English datasets (MMbench, ScienceQA, MME, POPE, AI2D). We assess the quality of these resources through qualitative and quantitative human analyses, measuring inter-annotator agreement. Additionally, we perform ablation studies to demonstrate the impact of multilingual data, with respect to English only, in VLMs training. Experiments, comprising 3 different models show that using multilingual, multimodal examples for training VLMs aids is consistently beneficial on non-English benchmarks, with positive transfer to English as well.

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