SynChart: Synthesizing Charts from Language Models

• URL: http://arxiv.org/abs/2409.16517v1
• Date: Wed, 25 Sep 2024 00:18:12 GMT
• Title: SynChart: Synthesizing Charts from Language Models
• Authors: Mengchen Liu, Qixiu Li, Dongdong Chen, Dong Chen, Jianmin Bao, Yunsheng Li,
• Abstract summary: 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. We trained a 4.2B chart-expert model using this dataset and achieve near-GPT-4O performance on the ChartQA task, surpassing GPT-4V.
• Score: 50.73888371511983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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