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Pre-trained language models have attracted increasing attention in thebiomedical domain, inspired by their great success in the general naturallanguage domain. Among the two main branches of pre-trained language models inthe general language domain, i.e., BERT (and its variants) and GPT (and itsvariants), the first one has been extensively studied in the biomedical domain,such as BioBERT and PubMedBERT. While they have achieved great success on avariety of discriminative downstream biomedical tasks, the lack of generationability constrains their application scope. In this paper, we propose BioGPT, adomain-specific generative Transformer language model pre-trained on largescale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks anddemonstrate that our model outperforms previous models on most tasks.Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDIend-to-end relation extraction tasks respectively, and 78.2% accuracy onPubMedQA, creating a new record. Our case study on text generation furtherdemonstrates the advantage of BioGPT on biomedical literature to generatefluent descriptions for biomedical terms. Code is available athttps://github.com/microsoft/BioGPT.

BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining