Residual Prompt Tuning: improving prompt tuning with residual reparameterization
Citations Over TimeTop 10% of 2023 papers
Abstract
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute <0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our experiments show that Residual Prompt Tuning significantly outperforms prompt tuning across T5-Large, T5-Base and BERT-Base models. Notably, our method reaches +7 points improvement over prompt tuning on SuperGLUE benchmark with T5-Base model and allows to reduce the prompt length by 10 times without hurting performance. In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings.
Related Papers
- → Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)(2021)2 cited
- → A New Initialization Method for Neural Networks with Weight Sharing(2021)2 cited
- → Remarks on the initialization of Caputo derivative(2012)4 cited
- The Distributed Initialization Algorithm Based on Known n MSs(2004)
- → Comparison of Random Weight Initialization to New Weight Initialization CONEXP(2020)