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Massively pre-trained transformer models are computationally expensive tofine-tune, slow for inference, and have large storage requirements. Recentapproaches tackle these shortcomings by training smaller models, dynamicallyreducing the model size, and by training light-weight adapters. In this paper,we propose AdapterDrop, removing adapters from lower transformer layers duringtraining and inference, which incorporates concepts from all three directions.We show that AdapterDrop can dynamically reduce the computational overhead whenperforming inference over multiple tasks simultaneously, with minimal decreasein task performances. We further prune adapters from AdapterFusion, whichimproves the inference efficiency while maintaining the task performancesentirely.

AdapterDrop: On the Efficiency of Adapters in Transformers