BioGPT

在生物医学领域，预训练语言模型引起了越来越多的关注，受到其在一般自然语言领域取得的巨大成功的启发。在一般语言领域的两个主要预训练语言模型分支中，即BERT（及其变种）和GPT（及其变种），第一个已经在生物医学领域得到了广泛研究，例如BioBERT和PubMedBERT。虽然它们在各种判别性生物医学下游任务上取得了巨大成功，但缺乏生成能力限制了它们的应用范围。在本文中，我们提出了BioGPT，一个在大规模生物医学文献上预训练的领域特定生成Transformer语言模型。我们对BioGPT在六个生物医学自然语言处理任务上进行了评估，并证明我们的模型在大多数任务上优于先前模型。特别是，在BC5CDR、KD-DTI和DDI端到端关系抽取任务中，我们分别获得了44.98%、38.42%和40.76%的F1评分，并在PubMedQA上获得了78.2%的准确率，创造了一个新纪录。我们对文本生成的案例研究进一步证明了BioGPT在生成生物医学术语的流畅描述方面的优势。

引用

如果您在您的研究中发现BioGPT有用，请引用以下论文：

@article{10.1093/bib/bbac409,
    author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan},
    title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}",
    journal = {Briefings in Bioinformatics},
    volume = {23},
    number = {6},
    year = {2022},
    month = {09},
    abstract = "{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 natural language processing 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.}",
    issn = {1477-4054},
    doi = {10.1093/bib/bbac409},
    url = {https://doi.org/10.1093/bib/bbac409},
    note = {bbac409},
    eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf},
}