PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Y-axis features such as major tick digit length, number of ticks, value range, and format introduce significant biases in multimodal models during chart-to-table tasks, with y-axis prompting improving performance for some models.
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Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.
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Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation
Y-axis features such as major tick digit length, number of ticks, value range, and format introduce significant biases in multimodal models during chart-to-table tasks, with y-axis prompting improving performance for some models.