VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation

Abstract:Multimodal large language models have advanced rapidly, yet most remain English-centric, as scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of high-quality non-English image-text supervision. Although multilingual text data is abundant, naive textual fine-tuning can disrupt vision-language alignment and induce catastrophic forgetting. We propose Vision-Free Adaptation (VFA), a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone. Specifically, we fine-tune a base LLM on multilingual text data to derive a multilingual task vector, which is then merged with the vision-aligned task vector of an MLLM. Experiments on five MLLMs across six multilingual multimodal benchmarks show consistent improvements while preserving both general multimodal and text-only capabilities. Moreover, using less than 2% of the text data, VFA narrows the gap to the fully multimodal-trained model, demonstrating its data efficiency.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.26155 [cs.CL]

(or arXiv:2608.26155v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.26155 Focus to learn more arXiv-issued DOI via DataCite

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