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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine by Luo, Yizhen; Zhang, Jiahuan; Fan, Siqi; Yang, Kai; Wu, Yushuai; Qiao, Mu; Nie, Zaiqing is a scholarly article available to read on EtoBox.

What is BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine about?

Foundation models (FMs) have exhibited remarkable performance across a wide range of downstream tasks in many domains. Nevertheless, general-purpose FMs often face challenges when confronted with domain-specific problems, due to their limited access to the proprietary training data in a particular domain. In biomedicine, there are various biological modalities, such as molecules, proteins, and cells, which are encoded by the language of life and exhibit significant modality gaps with human natural language. In this paper, we introduce BioMedGPT, an open multimodal generative pre-trained transformer (GPT) for biomedicine, to bridge the gap between the language of life and human natural language. BioMedGPT allows users to easily ``communicate'' with diverse biological modalities through free text, which is the first of its kind. BioMedGPT aligns different biological modalities with natural language via a large generative language model, namely, BioMedGPT-LM. We publish BioMedGPT-10B, which unifies the feature spaces of molecules, proteins, and natural language via encoding and alignment. Through fine-tuning, BioMedGPT-10B outperforms or is on par with human and significantly larger g

Author

Luo, Yizhen; Zhang, Jiahuan; Fan, Siqi; Yang, Kai; Wu, Yushuai; Qiao, Mu; Nie, Zaiqing

Published

2023

Language

EN