ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
It addresses a growing need for multimodal models in chemistry, enabling better handling of molecular structures and reactions, but is incremental as it builds on existing multimodal LLM approaches.
The paper tackles the problem of processing visual information in chemical tasks by introducing ChemVLM, a multimodal large language model that integrates textual and visual data, achieving competitive performance across tasks like Chemical OCR, Multimodal Chemical Reasoning, and Multimodal Molecule Understanding.
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.