VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation
Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang Research, Microsoft, Lingjie Jiang Peking University, Shaohan Huang, Yun Chen Shanghai University of Finance and Economics, Guanhua Chen Southern University of Science and Technology 等 9 位作者
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, VFA attains competitive performance with a fully multimodally trained model using less than 2% of the text data, demonstrating its efficiency and effectiveness.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表