ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908610396684288 |
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| author | Li, Junxian Zhang, Di Wang, Xunzhi Hao, Zeying Lei, Jingdi Tan, Qian Zhou, Cai Liu, Wei Yang, Yaotian Xiong, Xinrui Wang, Weiyun Chen, Zhe Wang, Wenhai Li, Wei Zhang, Shufei Su, Mao Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan |
| author_facet | Li, Junxian Zhang, Di Wang, Xunzhi Hao, Zeying Lei, Jingdi Tan, Qian Zhou, Cai Liu, Wei Yang, Yaotian Xiong, Xinrui Wang, Weiyun Chen, Zhe Wang, Wenhai Li, Wei Zhang, Shufei Su, Mao Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan |
| contents | Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce \textbf{ChemVLM}, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks. Our model can be found at https://huggingface.co/AI4Chem/ChemVLM-26B. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area Li, Junxian Zhang, Di Wang, Xunzhi Hao, Zeying Lei, Jingdi Tan, Qian Zhou, Cai Liu, Wei Yang, Yaotian Xiong, Xinrui Wang, Weiyun Chen, Zhe Wang, Wenhai Li, Wei Zhang, Shufei Su, Mao Ouyang, Wanli Li, Yuqiang Zhou, Dongzhan Machine Learning Computer Vision and Pattern Recognition Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce \textbf{ChemVLM}, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks. Our model can be found at https://huggingface.co/AI4Chem/ChemVLM-26B. |
| title | ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.07246 |