When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations

• URL: http://arxiv.org/abs/2603.22368v1
• Date: Mon, 23 Mar 2026 05:34:47 GMT
• Title: When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations
• Abstract summary: We evaluate Vision Language Models (VLMs) on misleading visualization-caption pairs grounded in a fine-grained taxonomy of reasoning errors.<n>We develop a benchmark that combines real-world visualization with human-authored, curated misleading captions.
• Score: 29.03749623289173
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models (VLMs) perform well on many chart understanding tasks, their ability to detect misleading visualizations, especially when deception arises from subtle reasoning errors in captions, remains poorly understood. Here, we evaluate VLMs on misleading visualization-caption pairs grounded in a fine-grained taxonomy of reasoning errors (e.g., Cherry-picking, Causal inference) and visualization design errors (e.g., Truncated axis, Dual axis, inappropriate encodings). To this end, we develop a benchmark that combines real-world visualization with human-authored, curated misleading captions designed to elicit specific reasoning and visualization error types, enabling controlled analysis across error categories and modalities of misleadingness. Evaluating many commercial and open-source VLMs, we find that models detect visual design errors substantially more reliably than reasoning-based misinformation, and frequently misclassify non-misleading visualizations as deceptive. Overall, our work fills a gap between coarse detection of misleading content and the attribution of the specific reasoning or visualization errors that give rise to it.

Related papers

• Where To Look? : Causal Tracing of Vision Encoders in VLM [0.6980863185266349]
  Vision-language models can describe an image with remarkable accuracy, yet what visual information actually drives their answers?<n>We investigate this question through causal tracing, and we observe that highly causal vision tokens often lie outside the target region.<n>We further investigate: can these models preserve visual structure when appearance cues are removed? and find that visual cues are exploited to understand visual structures.
  arXiv  Detail & Related papers  (2026-08-11T10:17:34Z)
• Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis [63.49939245944781]
  We introduce FG-BMK, a comprehensive fine-grained evaluation benchmark containing 1.01 million questions and 0.28 million images.<n>We find that current LVLMs remain inadequate fine-grained recognizers, with failures arising from intertwined bottlenecks in visual representations, semantic grounding, modality alignment, and category-level knowledge.
  arXiv  Detail & Related papers  (2026-06-17T13:23:26Z)
• Diagnosing Visual Ignorance in Vision-Language Models [29.2851901986069]
  Vision-Language Models (VLMs) frequently rely on language priors, producing confident answers that are weakly grounded in visual evidence.<n>In this work, we study language-prior reliance from both mechanistic and behavioral perspectives.<n>We find that language-prior reliance is a systematic routing failure affecting both model internals and benchmark validity.
  arXiv  Detail & Related papers  (2026-06-05T04:16:01Z)
• Unveiling the Visual Counting Bottleneck in Vision-Language Models [49.591496870141846]
  This work deconstructs visual counting into three cognitive stages: visual individuation, magnitude awareness, and symbolic mapping.<n>Using synthetic Go boards and linear probes, we demonstrate that visual backbones maintain robust, linearly separable representations of quantity well into the extrapolation regime.<n>We pinpoint the collapse to the symbolic mapping stage, where the model fails to project valid visual magnitudes onto symbolic tokens.
  arXiv  Detail & Related papers  (2026-05-28T16:20:29Z)
• Diagnosing Bottlenecks in Data Visualization Understanding by Vision-Language Models [25.564425023762045]
  Current vision-language models (VLMs) still struggle on basic data visualization understanding tasks.<n>Are VLM failures attributable to limitations in how visual information in the data visualization is encoded, how information is transferred between the vision and language modules, or how information is processed within the language module?<n>We developed FUGU, a suite of data visualization understanding tasks, to precisely characterize potential sources of difficulty.
  arXiv  Detail & Related papers  (2025-10-02T18:29:07Z)
• Is this chart lying to me? Automating the detection of misleading visualizations [74.26574031329689]
  Misleading visualizations are a potent driver of misinformation on social media and the web.<n>We introduce Misviz, a benchmark of 2,604 real-world visualizations annotated with 12 types of misleaders.<n>We also release Misviz-synth, a synthetic dataset of 81,814 visualizations generated using Matplotlib and based on real-world data tables.
  arXiv  Detail & Related papers  (2025-08-29T14:36:45Z)
• The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models [11.500090488046899]
  Vision-Language Models (VLMs) are increasingly used to interpret visualizations, especially by non-expert users.<n>This study analyzes over 16,000 responses from ten different models across eight distinct types of misleading chart designs.<n>Our findings highlight the need for robust safeguards in VLMs against visual misinformation.
  arXiv  Detail & Related papers  (2025-08-13T11:11:18Z)
• ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs [98.27348724529257]
  We introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions.<n>Models trained with the ViCrit Task exhibit substantial gains across a variety of vision-language models benchmarks.
  arXiv  Detail & Related papers  (2025-06-11T19:16:54Z)
• BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models [2.526146573337397]
  We propose a new evaluation methodology, inspired by ophthalmologic diagnostics.<n>We use procedural generation of synthetic images to obtain control over visual attributes.<n>This diagnostic allows systematic stress testing and fine-grained failure analysis.
  arXiv  Detail & Related papers  (2025-06-05T12:43:10Z)
• Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios [69.00444996464662]
  We propose RIV-CoT, a Retrieval-Based Interleaved Visual Chain-of-Thought method that enables vision-language models to reason using visual crops corresponding to relevant entities.<n>Our experiments demonstrate that RIV-CoT improves answer accuracy by 3.1% and reasoning accuracy by 4.6% over vanilla CoT prompting.
  arXiv  Detail & Related papers  (2025-01-08T18:31:16Z)
• Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning [90.13978453378768]
  We introduce a comprehensive typology of factual errors in generated chart captions. A large-scale human annotation effort provides insight into the error patterns and frequencies in captions crafted by various chart captioning models. Our analysis reveals that even state-of-the-art models, including GPT-4V, frequently produce captions laced with factual inaccuracies.
  arXiv  Detail & Related papers  (2023-12-15T19:16:21Z)
• Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem [60.0878532426877]
  We propose a novel collaborative learning scheme from the viewpoint of visual perturbation calibration. Specifically, we devise a visual controller to construct two sorts of curated images with different perturbation extents. The experimental results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-07-24T23:50:52Z)

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.