arXiv:2605.31401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) largely follow the text-only LLM trajectory, excelling on English benchmarks but sharply degrading on low-resource languages, where neither large-scale image-text corpora nor culturally grounded evaluations exist. We present a systematic study of building a language-specific VLM for Romanian, covering the full pipeline from data construction to architectural choices. We translate established English VLM training and evaluation corpora into Romanian, applying machine translation to textual annotations and to in-image
The proliferation of Large Language Models (LLMs) has highlighted the linguistic and cultural bias towards English, prompting efforts to adapt these technologies for other languages now that the core capabilities are established.
This research provides a concrete methodology for extending advanced AI capabilities to low-resource languages, demonstrating a pathway for reducing linguistic dependency and fostering localized AI development beyond major tech hubs.
The explicit methodology for building language-specific Vision-Language Models (VLMs) by translating established English corpora signifies a shift towards more inclusive AI development, potentially reducing the dominance of English-centric models and data.
- · Non-English speaking nations
- · AI researchers in low-resource language communities
- · Local content creators and businesses
- · Multilingual AI platforms
- · English-only VLM incumbents (indirect)
- · Data scarcity for low-resource languages (reduced loss)
- · Cultural biases in AI
Increased availability and performance of VLMs for Romanian and potentially other low-resource languages.
Accelerated development of country-specific or region-specific AI applications and services based on these localized models.
Further diversification of the global AI landscape, fostering local innovation centers and reducing technological dependence on a few dominant languages and cultures.
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