Poster
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 13th 06:30 -- 09:00 PM @ Pacific Ballroom #102

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.

Author Information

Neil Houlsby (Google)
Andrei Giurgiu (Google)
Stanislaw Jastrzebski (New York University)
Bruna Morrone (Google)
Quentin de Laroussilhe (Google Brain)
Andrea Gesmundo (Google)
Mona Attariyan (Google)
Sylvain Gelly (Google Brain)

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