SynChart: Synthesizing Charts from Language Models
<|reference_start|>SynChart: Synthesizing Charts from Language Models: With the release of GPT-4V(O), its use in generating pseudo labels for multi-modality tasks has gained significant popularity. However, it is still a secret how to build such advanced models from its base large language models (LLMs). This work explores the potential of using LLMs alone for data generation and develop competitive multi-modality models focusing on chart understanding. We construct a large-scale chart dataset, SynChart, which contains approximately 4 million diverse chart images with over 75 million dense annotations, including data tables, code, descriptions, and question-answer sets. We trained a 4.2B chart-expert model using this dataset and achieve near-GPT-4O performance on the ChartQA task, surpassing GPT-4V.<|reference_end|>
@article{liu2024synchart:,
    title={SynChart: Synthesizing Charts from Language Models},
    author={Mengchen Liu, Qixiu Li, Dongdong Chen, Dong Chen, Jianmin Bao,
  Yunsheng Li},
    journal={arXiv preprint arXiv:2409.16517},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16517},
    primaryClass={cs.AI}
}