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ChartCheck: Explainable Fact-Checking over Real-World Chart Images

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arxiv 2311.07453 v2 pith:4FOWSRBF submitted 2023-11-13 cs.CL cs.CV

Mubashara Akhtar , Nikesh Subedi , Vivek Gupta , Sahar Tahmasebi , Oana Cocarascu , Elena Simperl This is my paper
classification cs.CLcs.CV
keywords chartschartcheckreal-worldchartcommunityexplainablefact-checkingmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and communicate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts

    cs.CL 2025-06 conditional novelty 7.0 of 10

    A large-scale benchmark shows that leading multimodal language models still underperform expert humans at verifying climate claims from scientific charts.

  2. How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

    cs.HC 2026-07 conditional novelty 6.5 of 10

    TIC is a process-oriented taxonomy of recurring issues in data communication, refined on 700 real-world narratives and mapped onto analysis, construction, and reception stages.

  3. Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts

    cs.HC 2025-05 conditional novelty 6.0 of 10

    Multimodal LLMs underperform humans at directly rating charts' experiential impact, but they are substantially better at pairwise comparisons, especially when the human ratings differ clearly.

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