@inproceedings{xue-etal-2026-benchmarking,
    title = "Benchmarking and Mitigating the Impact of Noisy User Prompts in Medical {VLM}s via Cross-Modal Reflection",
    author = "Xue, Zhiyu  and
      Abbasi-Asl, Reza  and
      Pedarsani, Ramtin",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.67/",
    pages = "900--914",
    ISBN = "979-8-89176-384-5",
    abstract = "Medical vision-language models (Med-VLMs) offer a new and effective paradigm for digital health in tasks such as disease diagnosis using clinical images and text. In these tasks, an important but underexplored research question is how Med-VLMs interpret and respond to user-provided clinical information, especially when the prompts are noisy. For a systematic evaluation, we construct Med-CP, a large-scale visual question answering (VQA) benchmark designed to comprehensively evaluate the influence of clinical prompts across diverse modalities, anatomical regions, and diagnostic tasks. Our experiments reveal that existing Med-VLMs tend to follow user-provided prompts blindly, regardless of whether they are accurate or not, raising concerns about their reliability in real-world interactions. To address this problem, we introduce a novel supervised fine-tuning (SFT) approach for Med-VLMs based on cross-modal reflection chain-of-thought (CoT) across medical images and text. In our SFT method, the Med-VLM is trained to produce reasoning paths for the analysis of the medical image and the user-provided prompt. Then, the final answer is determined by conducting a reflection on the visual and textual information. Experimental results demonstrate that our method considerably enhances the robustness against noisy user-provided prompts for both in-domain and out-of-domain evaluation scenarios."
}