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MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning

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arxiv 2311.10774 v2 pith:VKZ74NFP submitted 2023-11-15 cs.CL cs.AI

Fuxiao Liu , Xiaoyang Wang , Wenlin Yao , Jianshu Chen , Kaiqiang Song , Sangwoo Cho , Yaser Yacoob , Dong Yu This is my paper
classification cs.CLcs.AI
keywords chartmultimodalchartsunderstandingbenchmarktaskstextbfcomprehensive
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rapid development of large language models (LLMs) and their integration into large multimodal models (LMMs), there has been impressive progress in zero-shot completion of user-oriented vision-language tasks. However, a gap remains in the domain of chart image understanding due to the distinct abstract components in charts. To address this, we introduce a large-scale MultiModal Chart Instruction (\textbf{MMC-Instruction}) dataset comprising 600k instances supporting diverse tasks and chart types. Leveraging this data, we develop MultiModal Chart Assistant (\textbf{MMCA}), an LMM that achieves state-of-the-art performance on existing chart QA benchmarks. Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts. Extensive experiments on MMC-Benchmark reveal the limitations of existing LMMs on correctly interpreting charts, even for the most recent GPT-4V model. Our work provides an instruction-tuning methodology and benchmark to advance multimodal understanding of charts. Code and data are available at https://github.com/FuxiaoLiu/MMC.

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