Multilingual VLM Training: Adapting an English-Trained VLM to French

• URL: http://arxiv.org/abs/2512.10336v1
• Date: Thu, 11 Dec 2025 06:38:51 GMT
• Title: Multilingual VLM Training: Adapting an English-Trained VLM to French
• Authors: Jules Lahmi, Alexis Roger,
• Abstract summary: This paper explores the challenges of adapting an English-trained VLM to different languages.<n>We consider a translation-based pipeline, LoRA finetuning, and a two-stage finetuning strategy that separates vision adaptation from language adaptation.<n>The results reveal that dataset translation remains a major bottleneck in multilingual VLM performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence has made great progress in recent years, particularly in the development of Vision--Language Models (VLMs) that understand both visual and textual data. However, these advancements remain largely limited to English, reducing their accessibility for non--English speakers. It is essential to extend these capabilities to a broader range of languages. This paper explores the challenges of adapting an English-trained VLM to different languages. To this end, we will explore and compare different methods for their performance and computational cost. We consider a translation-based pipeline, LoRA finetuning, and a two-stage finetuning strategy that separates vision adaptation from language adaptation. To evaluate these methods, we use a combination of standard multimodal benchmarks translated into the target language and manual assessments by native experts. The results reveal that dataset translation remains a major bottleneck in multilingual VLM performance, with data quality limiting the effectiveness of training and evaluation. These findings suggest that future efforts should focus on native-language dataset collection and improved translation strategies.

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