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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.16517 |
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| _version_ | 1866914956824281088 |
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| author | Liu, Mengchen Li, Qixiu Chen, Dongdong Chen, Dong Bao, Jianmin Li, Yunsheng |
| author_facet | Liu, Mengchen Li, Qixiu Chen, Dongdong Chen, Dong Bao, Jianmin Li, Yunsheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SynChart: Synthesizing Charts from Language Models Liu, Mengchen Li, Qixiu Chen, Dongdong Chen, Dong Bao, Jianmin Li, Yunsheng Artificial Intelligence 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. |
| title | SynChart: Synthesizing Charts from Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.16517 |