@inproceedings{zhang-etal-2026-images,
    title = "Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in {VLM}s",
    author = "Zhang, Chi  and
      Ding, Wenxuan  and
      Liu, Jiale  and
      Wu, Mingrui  and
      Wu, Qingyun  and
      Mooney, Ray",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.323/",
    pages = "6872--6895",
    ISBN = "979-8-89176-380-7",
    abstract = "Vision-Language Models (VLMs) have shown strong multimodal reasoning capability on Visual-Question-Answering (VQA) benchmarks. However, their robustness against textual misinformation remains under-explored. While existing research has extensively studied the effect of misinformation in text-only domains, it is not clear how VLMs arbitrate between contradictory information from different modalities. To bridge the gap, we first propose the ConText-VQA (i.e. Conflicting Text) dataset, consisting of image-question pairs together with systematically generated persuasive prompts that deliberately conflict with visual evidence. Then, a thorough testing framework is designed and executed to benchmark the susceptibility of various models to these conflicting textual inputs. Comprehensive experiments over 11 state-of-the-art VLMs reveal that these models are indeed vulnerable to misleading prompts, often overriding clear visual evidence in favor of the conflicting text, and show an average performance drop of over 48.2{\%} after only one round of persuasive conversation. Our findings highlight a critical limitation in current VLMs and underscore the need for improved robustness against textual manipulation."
}