@inproceedings{wu-etal-2026-medkinstruct,
    title = "{M}ed{KI}nstruct: A Multimodal Knowledge Graph Based Framework for Multi-Hop and Hard-Negative Instruction Data Synthesis in {M}ed{VQA}",
    author = "Wu, Yinan  and
      Jin, Jihang  and
      Bao, Xuhao  and
      Zhang, Weiyan  and
      Yan, Hanjing  and
      Ruan, Tong  and
      Wang, ChunMing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1391/",
    pages = "27935--27947",
    ISBN = "979-8-89176-395-1",
    abstract = "Medical visual question answering (MedVQA) requires models to provide accurate answers given a medical image and a corresponding question. Recently, instruction tuning of general large vision{--}language models (LVLMs) has become a dominant paradigm for this task, enabling open-ended predictions and effective integration of multimodal information. However, existing methods synthesize instruction data from image{--}caption pairs that primarily focus on visual attributes, rather than knowledge-level QA generation. This situation limits the model{'}s ability to learn relevant medical knowledge during training, thereby restricting its performance on MedVQA. Hence, this paper proposes MedKInstruct, which incorporates a multimodal medical knowledge graph (MMKG) to assist LVLMs in synthesizing knowledge-intensive instruction data. Additionally, we design an MMKG path{--}based reward function to train a stronger MedVQA model through reinforcement learning. Experimental results on the public datasets Slake and VQA-RAD show that MedKInstruct outperforms previous methods by 4.16{\%} and 4.50{\%}. The source code is available at the following link: https://github.com/Sonder-hang/MedKinstruct"
}