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Oral
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly

Thu Jun 13 11:20 AM -- 11:25 AM (PDT) @ Room 104
Fine-tuning large pretrained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to $26$ diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within $0.8\%$ of the performance of full fine-tuning, adding only $3.6\%$ parameters per task. By contrast, fine-tuning trains $100\%$ of the parameters per task.