ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via
  Chart-to-Code Generation
Authors: name: Chufan Shi, name: Cheng Yang, name: Yaxin Liu, name: Bo Shui, name: Junjie Wang, name: Mohan Jing, name: Linran Xu, name: Xinyu Zhu, name: Siheng Li, name: Yuxiang Zhang, name: Gongye Liu, name: Xiaomei Nie, name: Deng Cai, name: Yujiu Yang

We introduce a new benchmark, ChartMimic, aimed at assessing the
visually-grounded code generation capabilities of large multimodal models
(LMMs). ChartMimic utilizes information-intensive visual charts and textual
instructions as inputs, requiring LMMs to generate the corresponding code for
chart rendering. ChartMimic includes 1,000 human-curated (figure, instruction,
code) triplets, which represent the authentic chart use cases found in
scientific papers across various domains(e.g., Physics, Computer Science,
Economics, etc). These charts span 18 regular types and 4 advanced types,
diversifying into 191 subcategories. Furthermore, we propose multi-level
evaluation metrics to provide an automatic and thorough assessment of the
output code and the rendered charts. Unlike existing code generation
benchmarks, ChartMimic places emphasis on evaluating LMMs' capacity to
harmonize a blend of cognitive capabilities, encompassing visual understanding,
code generation, and cross-modal reasoning. The evaluation of 3 proprietary
models and 11 open-weight models highlights the substantial challenges posed by
ChartMimic. Even the advanced GPT-4V, Claude-3-opus only achieve an average
score of 73.2 and 53.7, respectively, indicating significant room for
improvement. We anticipate that ChartMimic will inspire the development of
LMMs, advancing the pursuit of artificial general intelligence.