Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large
  Language Models in Code Generation from Scientific Plots
Authors: name: Chengyue Wu; name: Yixiao Ge; name: Qiushan Guo; name: Jiahao Wang; name: Zhixuan Liang; name: Zeyu Lu; name: Ying Shan; name: Ping Luo
Published: 2024-05-13 17:59:22

Abstract: The remarkable progress of Multi-modal Large Language Models (MLLMs) has
attracted significant attention due to their superior performance in visual
contexts. However, their capabilities in turning visual figure to executable
code, have not been evaluated thoroughly. To address this, we introduce
Plot2Code, a comprehensive visual coding benchmark designed for a fair and
in-depth assessment of MLLMs. We carefully collect 132 manually selected
high-quality matplotlib plots across six plot types from publicly available
matplotlib galleries. For each plot, we carefully offer its source code, and an
descriptive instruction summarized by GPT-4. This approach enables Plot2Code to
extensively evaluate MLLMs' code capabilities across various input modalities.
Furthermore, we propose three automatic evaluation metrics, including code pass
rate, text-match ratio, and GPT-4V overall rating, for a fine-grained
assessment of the output code and rendered images. Instead of simply judging
pass or fail, we employ GPT-4V to make an overall judgement between the
generated and reference images, which has been shown to be consistent with
human evaluation. The evaluation results, which include analyses of 14 MLLMs
such as the proprietary GPT-4V, Gemini-Pro, and the open-sourced Mini-Gemini,
highlight the substantial challenges presented by Plot2Code. With Plot2Code, we
reveal that most existing MLLMs struggle with visual coding for text-dense
plots, heavily relying on textual instruction. We hope that the evaluation
results from Plot2Code on visual coding will guide the future development of
MLLMs. All data involved with Plot2Code are available at
https://huggingface.co/datasets/TencentARC/Plot2Code.