Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs

• URL: http://arxiv.org/abs/2601.19202v1
• Date: Tue, 27 Jan 2026 05:04:38 GMT
• Title: Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs
• Authors: Chi Zhang, Wenxuan Ding, Jiale Liu, Mingrui Wu, Qingyun Wu, Ray Mooney,
• Abstract summary: Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks.<n>We show that these models are indeed vulnerable to misleading textual prompts, often overriding clear visual evidence in favor of the conflicting text.
• Score: 17.56537230934894
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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 systematically 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% 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.

Related papers

• Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs [80.03370593724422]
  Out-of-distribution (OOD) detection seeks to identify samples from unknown classes.<n>Current methods often incorporate intra-modal distance during OOD detection, such as comparing negative texts with ID labels.<n>We propose InterNeg, a framework that systematically utilizes consistent inter-modal distance enhancement from textual and visual perspectives.
  arXiv  Detail & Related papers  (2026-03-03T05:44:47Z)
• VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text? [51.02924254085878]
  Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs.<n>We introduce VISTA-Bench, a benchmark from multimodal perception, reasoning, to unimodal understanding domains.
  arXiv  Detail & Related papers  (2026-02-04T17:48:55Z)
• DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection [6.225860651499494]
  Multimodal fake news detection is crucial for mitigating adversarial misinformation.<n>We propose DIVER (Dynamic Iterative Visual Evidence Reasoning), a framework grounded in a progressive, evidence-driven reasoning paradigm.<n>Experiments on Weibo, Weibo21, and GossipCop demonstrate that DIVER outperforms state-of-the-art baselines by an average of 2.72%.
  arXiv  Detail & Related papers  (2026-01-12T04:01:33Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• Multimodal Fact Checking with Unified Visual, Textual, and Contextual Representations [2.139909491081949]
  We propose a unified framework for fine-grained multimodal fact verification called "MultiCheck"<n>Our architecture combines dedicated encoders for text and images with a fusion module that captures cross-modal relationships using element-wise interactions.<n>We evaluate our approach on the Factify 2 dataset, achieving a weighted F1 score of 0.84, substantially outperforming the baseline.
  arXiv  Detail & Related papers  (2025-08-07T07:36:53Z)
• Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models [93.46875303598577]
  Vision-language models (VLMs) have advanced rapidly in processing multimodal information, but their ability to reconcile conflicting signals remains underexplored.<n>This work investigates how VLMs process ASCII art, a unique medium where textual elements collectively form visual patterns, potentially creating semantic-visual conflicts.
  arXiv  Detail & Related papers  (2025-04-02T10:47:07Z)
• Words or Vision: Do Vision-Language Models Have Blind Faith in Text? [34.88114876390461]
  Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks.<n>We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings.<n>We discover a emphblind faith in text'' phenomenon:VLMs disproportionately trust textual data over visual data when inconsistencies arise.
  arXiv  Detail & Related papers  (2025-03-04T02:21:07Z)
• SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models [6.52323086990482]
  Vision language models (VLM) demonstrate sophisticated multimodal reasoning yet are prone to hallucination when confronted with knowledge conflicts.<n>This research introduces segsub, a framework for applying targeted image perturbations to investigate VLM resilience against knowledge conflicts.
  arXiv  Detail & Related papers  (2025-02-19T00:26:38Z)
• ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models [92.60282074937305]
  We introduce ConTextual, a novel dataset featuring human-crafted instructions that require context-sensitive reasoning for text-rich images. We conduct experiments to assess the performance of 14 foundation models and establish a human performance baseline. We observe a significant performance gap of 30.8% between GPT-4V and human performance.
  arXiv  Detail & Related papers  (2024-01-24T09:07:11Z)
• Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information [79.06082391992545]
  We propose an Efficient Context-aware model (ECASE) that fully exploits contextual information. We introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-08T08:47:10Z)
• A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues [23.743431157431893]
  Conditional inference on joint textual and visual clues is a multi-modal reasoning task. We propose a Multi-modal Context Reasoning approach, named ModCR. We conduct extensive experiments on two corresponding data sets and experimental results show significantly improved performance.
  arXiv  Detail & Related papers  (2023-05-08T08:05:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.