Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their robustness against textual misinformation remains under-explored. While existing research has 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 system-atically generated persuasive prompts that deliberately conflict with visual evidence. Then, a thorough evaluation framework is designed and executed to benchmark the susceptibility of various models to these conflicting multimodal inputs. Comprehensive experiments over 11 state-of-the-art VLMs reveal that these models are indeed vulnerable to misleading textual prompts, often overriding clear visual evidence in favor of the conflicting text, and show an average performance drop of over 48.2 percent 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.

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European Chapter of the Association for Computational Linguistics (EACL) (2026).

@article{zhang:eacl26, title={Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs}, author={Chi Zhang and Wenxuan Ding and Jiale Liu and Mingrui Wu and Qingyun Wu and Ray Mooney}, booktitle={European Chapter of the Association for Computational Linguistics (EACL)}, month={March}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=128144", year={2026} }