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

📝 Original Info

• Title: Multilingual VLM Training: Adapting an English-Trained VLM to French
• ArXiv ID: 2512.10336
• Date: 2025-12-11
• Authors: Jules Lahmi, Alexis Roger

📝 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.

📄 Full Content

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