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MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems

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arxiv 2410.14179 v2 pith:KY7KCVWH submitted 2024-10-18 cs.CL cs.CV

Zifeng Zhu , Mengzhao Jia , Zhihan Zhang , Lang Li , Meng Jiang This is my paper · ORCID
classification cs.CLcs.CV
keywords answeringmllmsmulti-chartmultichartqaquestionreasoningtasksbenchmarks
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Multimodal Large Language Models (MLLMs) have demonstrated impressive abilities across various tasks, including visual question answering and chart comprehension, yet existing benchmarks for chart-related tasks fall short in capturing the complexity of real-world multi-chart scenarios. Current benchmarks primarily focus on single-chart tasks, neglecting the multi-hop reasoning required to extract and integrate information from multiple charts, which is essential in practical applications. To fill this gap, we introduce MultiChartQA, a benchmark that evaluates MLLMs' capabilities in four key areas: direct question answering, parallel question answering, comparative reasoning, and sequential reasoning. Our evaluation of a wide range of MLLMs reveals significant performance gaps compared to humans. These results highlight the challenges in multi-chart comprehension and the potential of MultiChartQA to drive advancements in this field. Our code and data are available at https://github.com/Zivenzhu/Multi-chart-QA

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Cited by 4 Pith papers

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

  1. DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards

    cs.CL 2025-08 unverdicted novelty 6.0 of 10

    DashboardQA is a new benchmark of 405 question-answer pairs over 112 interactive dashboards; the strongest tested GUI agent reaches only 38.69% accuracy.

  2. ChartGen: Scaling Chart Understanding Via Code-Guided Synthetic Chart Generation

    cs.HC 2025-05 conditional novelty 6.0 of 10

    A fully automated pipeline generates a 222.5K-pair synthetic chart dataset with 27 chart types and 11 plotting libraries, and a GPT-4o-judged benchmark shows current open-weights VLMs still underperform on chart-to-co...

  3. MageBench: Bridging Large Multimodal Models to Agents

    cs.CV 2024-12 conditional novelty 6.0 of 10

    MageBench introduces a 483-scenario benchmark showing current large multimodal models are far weaker than humans at agent tasks requiring continuous visual feedback and planning.

  4. Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models

    cs.AI 2025-04 conditional novelty 4.0 of 10

    A structured survey of reinforcement-learning-based reasoning methods for multimodal large language models, including a taxonomy, reward design review, benchmark tables, and open challenges.

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