Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts

• URL: http://arxiv.org/abs/2604.21344v1
• Date: Thu, 23 Apr 2026 06:59:56 GMT
• Title: Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
• Abstract summary: We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images.<n>We evaluate the performance of nine state-of-the-art Multimodal Language Models (MLMs) on PolyChartQA across question type, difficulty, question source, and key structural characteristics of multi-charts.
• Score: 3.2880869992413246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Charts are widely used to present complex information. Deriving meaningful insights in real-world contexts often requires interpreting multiple related charts together. Research on understanding multi-chart images has not been extensively explored. We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images. PolyChartQA comprises 534 multi-chart images (with a total of 2,297 sub-charts) sourced from peer-reviewed computer science research publications and 2,694 QA pairs. We evaluate the performance of nine state-of-the-art Multimodal Language Models (MLMs) on PolyChartQA across question type, difficulty, question source, and key structural characteristics of multi-charts. Our results show a 27.4% LLM-based accuracy (L-Accuracy) drop on human-authored questions compared to MLM-generated questions, and a 5.39% L-accuracy gain with our proposed prompting method.

Related papers

• LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning [25.04804547776196]
  Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities.<n>Existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insufficient to evaluate these capabilities.<n>We introduce LongChart, a benchmark whose VQA sets contain an average of 6.5 images and 31.2 questions.<n>We evaluate 10 state-of-the-art MLLMs and examine three factors that influence performance: reasoning patterns, auxiliary tools, and robustness to image perturbations.
  arXiv  Detail & Related papers  (2026-08-02T15:48:04Z)
• ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning [54.86473583610112]
  We propose PointCoT, which integrates reflective interaction into chain-of-thought reasoning in charts.<n>By prompting MLLMs to generate bounding boxes and re-render charts based on location annotations, we establish connections between textual reasoning steps and visual grounding regions.<n>We develop two instruction-tuned models, ChartPointQ2 and ChartPointQ2.5, which outperform state-of-the-art across several chart benchmarks.
  arXiv  Detail & Related papers  (2025-11-29T04:01:55Z)
• POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering [69.52231076699756]
  PolyChartQA is the first large-scale multilingual chart question answering benchmark covering 22,606 charts and 26,151 question-answering pairs across 10 diverse languages.<n>We leverage state-of-the-art LLM-based translation and enforce rigorous quality control in the pipeline to ensure the linguistic and semantic consistency of the generated multilingual charts.
  arXiv  Detail & Related papers  (2025-07-16T06:09:02Z)
• InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts [20.95897015354387]
  InfoChartQA is a benchmark for evaluating multimodal large language models (MLLMs) on infographic chart understanding.<n>It includes 5,642 pairs of infographic and plain charts, each sharing the same underlying data but differing in visual presentations.<n>We design visual-element-based questions to capture their unique visual designs and communicative intent.
  arXiv  Detail & Related papers  (2025-05-25T08:28:03Z)
• ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering [27.58410749367183]
  We introduce ChartQAPro, a new benchmark that includes 1,341 charts from 157 diverse sources, spanning various chart types.<n>Our evaluations with 21 models show a substantial performance drop for LVLMs on ChartQAPro.
  arXiv  Detail & Related papers  (2025-04-07T21:05:06Z)
• Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering [45.67334913593117]
  Misleading visualizations pose risks to public understanding and raise safety concerns for AI systems involved in data-driven communication.<n>We benchmark 24 state-of-the-art MLLMs, analyze their performance across misleader types and chart formats, and propose a novel region-aware reasoning pipeline.<n>Our work lays the foundation for developing MLLMs that are robust, trustworthy, and aligned with the demands of responsible visual communication.
  arXiv  Detail & Related papers  (2025-03-23T18:56:33Z)
• Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts [62.45232157149698]
  We introduce a novel Chart Hypothetical Question Answering (HQA) task, which imposes assumptions on the same question to compel models to engage in counterfactual reasoning based on the chart content.<n> Furthermore, we introduce HAI, a human-AI interactive data synthesis approach that leverages the efficient text-editing capabilities of MLLMs alongside human expert knowledge to generate diverse and high-quality HQA data at a low cost.
  arXiv  Detail & Related papers  (2025-03-06T05:08:40Z)
• Distill Visual Chart Reasoning Ability from LLMs to MLLMs [64.32993770646165]
  Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs)<n>We propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs.<n>ReachQA is a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities of MLLMs.
  arXiv  Detail & Related papers  (2024-10-24T14:50:42Z)
• MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems [18.188725200923333]
  Existing benchmarks for chart-related tasks fall short in capturing the complexity of real-world multi-chart scenarios.<n>We introduce MultiChartQA, a benchmark that evaluates MLLMs' capabilities in four key areas: direct question answering, parallel question answering, comparative reasoning, and sequential reasoning.<n>Our results highlight the challenges in multi-chart comprehension and the potential of MultiChartQA to drive advancements in this field.
  arXiv  Detail & Related papers  (2024-10-18T05:15:50Z)
• CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs [62.84082370758761]
  CharXiv is a comprehensive evaluation suite involving 2,323 charts from arXiv papers. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model.
  arXiv  Detail & Related papers  (2024-06-26T17:50:11Z)
• ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning [55.22996841790139]
  We benchmark the ability of off-the-shelf Multi-modal Large Language Models (MLLMs) in the chart domain.<n>We construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data.<n>We develop ChartVLM to offer a new perspective on handling multi-modal tasks that strongly depend on interpretable patterns.
  arXiv  Detail & Related papers  (2024-02-19T14:48:23Z)

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.