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

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 atthis https URL

@article{zhu2025_2410.14179,
  title={ MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems },
  author={ Zifeng Zhu and Mengzhao Jia and Zhihan Zhang and Lang Li and Meng Jiang },
  journal={arXiv preprint arXiv:2410.14179},
  year={ 2025 }
}