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.

翻译：预训练语言模型在通用自然语言领域取得巨大成功，受此启发其在生物医学领域也日益受到关注。在通用语言领域的两大预训练语言模型分支——即BERT（及其变体）与GPT（及其变体）中，前者已在生物医学领域得到广泛研究，例如BioBERT和PubMedBERT。尽管这些模型在各类判别式下游生物医学任务上取得了显著成功，但生成能力的不足限制了其应用范围。本文提出BioGPT——一种在大规模生物医学文献上预训练的领域特定生成式Transformer语言模型。我们在六项生物医学NLP任务上评估BioGPT，结果表明该模型在大部分任务上优于先前模型。特别地，我们在BC5CDR、KD-DTI和DDI端到端关系抽取任务上分别取得44.98%、38.42%和40.76%的F1分数，在PubMedQA上取得78.2%的准确率，刷新了纪录。文本生成的案例研究进一步证明BioGPT在生物医学文献中生成生物学术语流畅描述的优势。代码已开源至 https://github.com/microsoft/BioGPT。