  In this work, we explore "prompt tuning", a simple yet effective mechanism
for learning "soft prompts" to condition frozen language models to perform
specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft
prompts are learned through backpropagation and can be tuned to incorporate
signal from any number of labeled examples. Our end-to-end learned approach
outperforms GPT-3's "few-shot" learning by a large margin. More remarkably,
through ablations on model size using T5, we show that prompt tuning becomes
more competitive with scale: as models exceed billions of parameters, our
method "closes the gap" and matches the strong performance of model tuning
(where all model weights are tuned). This finding is especially relevant in
that large models are costly to share and serve, and the ability to reuse one
frozen model for multiple downstream tasks can ease this burden. Our method can
be seen as a simplification of the recently proposed "prefix tuning" of Li and
Liang (2021), and we provide a comparison to this and other similar approaches.
Finally, we show that conditioning a frozen model with soft prompts confers
benefits in robustness to domain transfer, as compared to full model tuning.
