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

• URL: http://arxiv.org/abs/2410.14179v1
• Date: Fri, 18 Oct 2024 05:15:50 GMT
• Title: MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
• Authors: Zifeng Zhu, Mengzhao Jia, Zhihan Zhang, Lang Li, Meng Jiang,
• Abstract summary: Existing benchmarks for chart-related tasks fall short in capturing the complexity of real-world multi-chart scenarios. 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 results highlight the challenges in multi-chart comprehension and the potential of MultiChartQA to drive advancements in this field.
• Score: 18.188725200923333
• License:
• Abstract: 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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