@inproceedings{li-etal-2026-vfa,
    title = "{VFA}: Empowering Multilingual {MLLM}s via Vision-Free Adaptation",
    author = "Li, Yixia  and
      Shi, Yaqing  and
      Ruan, Zhiwen  and
      Zhang, Dongdong  and
      Jiang, Lingjie  and
      Huang, Shaohan  and
      Chen, Yun  and
      Chen, Guanhua  and
      Wei, Furu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.407/",
    pages = "8998--9015",
    ISBN = "979-8-89176-390-6",
    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, VFA attains competitive performance with a fully multimodally trained model using less than 2{\%} of the text data, demonstrating its efficiency and effectiveness."
}