Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference

• URL: http://arxiv.org/abs/2304.04947v2
• Date: Sun, 26 Nov 2023 22:00:36 GMT
• Title: Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
• Authors: Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang
• Abstract summary: Conditional Adapter (CoDA) is a parameter-efficient transfer learning method that also improves inference efficiency. Across a variety of language, vision, and speech tasks, CoDA achieves a 2x to 8x inference speed-up.
• Score: 53.65845680932835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Conditional Adapter (CoDA), a parameter-efficient transfer learning method that also improves inference efficiency. CoDA generalizes beyond standard adapter approaches to enable a new way of balancing speed and accuracy using conditional computation. Starting with an existing dense pretrained model, CoDA adds sparse activation together with a small number of new parameters and a light-weight training phase. Our experiments demonstrate that the CoDA approach provides an unexpectedly efficient way to transfer knowledge. Across a variety of language, vision, and speech tasks, CoDA achieves a 2x to 8x inference speed-up compared to the state-of-the-art Adapter approaches with moderate to no accuracy loss and the same parameter efficiency.

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