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Prompt tuning is one of the successful approaches for parameter-efficienttuning of pre-trained language models. Despite being arguably the mostparameter-efficient (tuned soft prompts constitute <0.1% of total parameters),it typically performs worse than other efficient tuning methods and is quitesensitive to hyper-parameters. In this work, we introduce Residual PromptTuning - a simple and efficient method that significantly improves theperformance and stability of prompt tuning. We propose to reparameterize softprompt embeddings using a shallow network with a residual connection. Ourexperiments show that Residual Prompt Tuning significantly outperforms prompttuning on SuperGLUE benchmark. Notably, our method reaches +7 pointsimprovement over prompt tuning with T5-Base and allows to reduce the promptlength by 10x without hurting performance. In addition, we show that ourapproach is robust to the choice of learning rate and prompt initialization,and is effective in few-shot settings.

Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization