CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal
  LLMs
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:
https://charxiv.github.io/
667cbc2b557fe99a9eff62d9
Zirui Wang
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2024-06-27T07:31:28.683Z
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Zirui Wang
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Mengzhou Xia
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2024-06-27T14:20:32.941Z
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Mengzhou Xia
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Luxi He
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Howard Chen
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Howard Chen
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Yitao Liu
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2024-06-27T14:21:51.169Z
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Yitao Liu
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Richard Zhu
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2024-06-27T15:58:07.901Z
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Richard Zhu
user
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Kaiqu Liang
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2024-06-27T14:22:15.810Z
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KAIQU LIANG
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Xindi Wu
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2024-06-27T14:22:22.566Z
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Xindi Wu
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Haotian Liu
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2024-06-27T14:22:43.317Z
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Haotian Liu
user
liuhaotian
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Sadhika Malladi
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2024-06-27T14:23:01.673Z
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Sadhika Malladi
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Alexis Chevalier
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2024-06-27T14:23:07.186Z
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Alexis Chevalier
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AlexChvl
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Sanjeev Arora
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Danqi Chen
admin_assigned
2024-06-27T14:23:21.405Z
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Danqi Chen
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CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal
  LLMs
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:
https://charxiv.github.io/
CharXiv evaluates the chart understanding capabilities of MLLMs using a diverse and challenging dataset, uncovering significant weaknesses in model performance compared to human capabilities.
Multimodal Large Language Models MLLMs CharXiv descriptive questions reasoning questions chart understanding