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Motivation: A perennial challenge for biomedical researchers and clinicalpractitioners is to stay abreast with the rapid growth of publications andmedical notes. Natural language processing (NLP) has emerged as a promisingdirection for taming information overload. In particular, large neural languagemodels facilitate transfer learning by pretraining on unlabeled text, asexemplified by the successes of BERT models in various NLP applications.However, fine-tuning such models for an end task remains challenging,especially with small labeled datasets, which are common in biomedical NLP.  Results: We conduct a systematic study on fine-tuning stability in biomedicalNLP. We show that finetuning performance may be sensitive to pretrainingsettings, especially in low-resource domains. Large models have potential toattain better performance, but increasing model size also exacerbatesfinetuning instability. We thus conduct a comprehensive exploration oftechniques for addressing fine-tuning instability. We show that thesetechniques can substantially improve fine-tuning performance for lowresourcebiomedical NLP applications. Specifically, freezing lower layers is helpful forstandard BERT-BASE models, while layerwise decay is more effective forBERT-LARGE and ELECTRA models. For low-resource text similarity tasks such asBIOSSES, reinitializing the top layer is the optimal strategy. Overall,domainspecific vocabulary and pretraining facilitate more robust models forfine-tuning. Based on these findings, we establish new state of the art on awide range of biomedical NLP applications.  Availability and implementation: To facilitate progress in biomedical NLP, werelease our state-of-the-art pretrained and fine-tuned models:https://aka.ms/BLURB.

Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing