[2406.18521] CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

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arXiv:2406.18521 (cs)

[Submitted on 26 Jun 2024]

Title:CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

Authors:Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, Danqi Chen<br>View a PDF of the paper titled CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs, by Zirui Wang and 12 other authors

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Abstract:Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts with template-based questions, leading to an over-optimistic measure of progress. We demonstrate that although open-source models can appear to outperform strong proprietary models on these benchmarks, a simple stress test with slightly different charts or questions can deteriorate performance by up to 34.5%. In this work, we propose CharXiv, a comprehensive evaluation suite involving 2,323 natural, challenging, and diverse charts from arXiv papers. CharXiv includes two types of questions: 1) descriptive questions about examining basic chart elements and 2) reasoning questions that require synthesizing information across complex visual elements in the chart. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Our results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%. All models lag far behind human performance of 80.5%, underscoring weaknesses in the chart understanding capabilities of existing MLLMs. We hope CharXiv facilitates future research on MLLM chart understanding by providing a more realistic and faithful measure of progress. Project page and leaderboard: this https URL

Comments:<br>121 pages, 90 figures

Subjects:

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:2406.18521 [cs.CL]

(or<br>arXiv:2406.18521v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2406.18521

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arXiv-issued DOI via DataCite

Submission history<br>From: Zirui Wang [view email]<br>[v1]<br>Wed, 26 Jun 2024 17:50:11 UTC (13,395 KB)

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