Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation

Chart2Code has recently received significant attention in the multimodal community due to its potential to reduce the burden of visualization and promote a more detailed understanding of charts. However, existing Chart2Coderelated training datasets suffer from at least one of the following issues: (1) limited scale, (2) limited type coverage, and (3) inadequate complexity. To address these challenges, we seek more diverse sources that better align with real-world user distributions and propose dual data synthesis pipelines: (1) Synthesize based on online plotting code. ( 2 ) Synthesize based on the chart images in the academic paper. We create a large-scale Chart2Code training dataset Chart2Code53, including 53 chart types, 130K Chart-code pairs based on the pipeline. Experimental results demonstrate that even with few parameters, the model finetuned on Chart2Code53 achieves state-ofthe-art performance on multiple Chart2Code benchmarks within open-source models 1 .