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ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning

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arxiv 2402.12185 v6 pith:BN7A3P3S submitted 2024-02-19 cs.CV

Renqiu Xia , Bo Zhang , Hancheng Ye , Xiangchao Yan , Qi Liu , Hongbin Zhou , Zijun Chen , Peng Ye
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Min Dou Botian Shi Junchi Yan Yu Qiao
This is my paper
classification cs.CV
keywords chartchartvlmchartxmulti-modalevaluationmllmsmodelsreasoning
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in reasoning based on the queried contents remains under-explored. In this paper, to comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in the chart domain, we construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data. Besides, we develop ChartVLM to offer a new perspective on handling multi-modal tasks that strongly depend on interpretable patterns, such as reasoning tasks in the field of charts or geometric images. We evaluate the chart-related ability of mainstream MLLMs and our ChartVLM on the proposed ChartX evaluation set. Extensive experiments demonstrate that ChartVLM surpasses both versatile and chart-related large models, achieving results comparable to GPT-4V. We believe that our study can pave the way for further exploration in creating a more comprehensive chart evaluation set and developing more interpretable multi-modal models. Both ChartX and ChartVLM are available at: https://github.com/Alpha-Innovator/ChartVLM

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