ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

作者：Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Zhiyuan Liu, Maosong Sun · 年份：2025 · DOI：10.18653/v1/2025.acl-long.363 · 被引用次数：3 · 研究领域：Model-Driven Software Engineering Techniques、Mathematics, Computing, and Information Processing、Business Process Modeling and Analysis

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks.However, interpreting charts with textual descriptions often leads to information loss, as it fails to fully capture the dense information embedded in charts.In contrast, parsing charts into code provides lossless representations that can effectively contain all critical details.Although existing open-source MLLMs have achieved success in chart understanding tasks, they still face two major challenges when applied to chart-to-code tasks: (1) Low executability and poor restoration of chart details in the generated code and (2) Lack of large-scale and diverse training data.To address these challenges, we propose ChartCoder, the first dedicated chart-to-code MLLM, which leverages Code LLMs as the language backbone to enhance the executability of the generated code.Furthermore, we introduce Chart2Code-160k, the first large-scale and diverse dataset for chartto-code generation, and propose the Snippetof-Thought (SoT) method, which transforms direct chart-to-code generation data into stepby-step generation.Experiments demonstrate that ChartCoder, with only 7B parameters, surpasses existing open-source MLLMs on chartto-code benchmarks, achieving superior chart restoration and code excitability.Our code is available at https://github.com/thunlp/ ChartCoder.89 seed code with 27 chart types Available functions and parameters