  Pre-trained language models have attracted increasing attention in the
biomedical domain, inspired by their great success in the general natural
language domain. Among the two main branches of pre-trained language models in
the general language domain, i.e., BERT (and its variants) and GPT (and its
variants), the first one has been extensively studied in the biomedical domain,
such as BioBERT and PubMedBERT. While they have achieved great success on a
variety of discriminative downstream biomedical tasks, the lack of generation
ability constrains their application scope. In this paper, we propose BioGPT, a
domain-specific generative Transformer language model pre-trained on large
scale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks and
demonstrate 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 DDI
end-to-end relation extraction tasks respectively, and 78.2% accuracy on
PubMedQA, creating a new record. Our case study on text generation further
demonstrates the advantage of BioGPT on biomedical literature to generate
fluent descriptions for biomedical terms. Code is available at
https://github.com/microsoft/BioGPT.
