What is Adapter Tuning?
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Answers ( 2 )
Adapter Tuning is a parameter-efficient fine-tuning method for large pre-trained NLP models like BERT. It introduces adapter modules into the transformer architecture, allowing for efficient transfer learning by training only a small number of additional parameters per task while keeping the original model parameters fixed. This approach reduces computational and storage requirements while maintaining performance comparable to full fine-tuning.
Adapter Tuning facilitates transfer learning for various NLP tasks, including text classification and question answering. It achieves this by training task-specific adapter modules on top of a shared pre-trained model, maintaining performance while minimizing parameter overhead.