Abstract
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training. However, in the context of NLU, prior work and our results reveal that existing methods of prompt tuning do not perform well for normal-sized pre-trained models and for hard sequence tasks, indicating lack of universality. We present a novel empirical finding that properly-optimized prompt tuning can be universally effective across a wide range of model scales and NLU tasks, where it matches the performance of fine-tuning while having only 0.1\%-3\% tuned parameters. Our method P-Tuning v2 is not a new method but a version of prefix-tuning \cite{li2021prefix} optimized and adapted for NLU. Given the universality and simplicity of P-Tuning v2, we believe it can serve as an alternative for fine-tuning and a strong baseline for future research.
Abstract (translated)
URL
https://arxiv.org/abs/2110.07602
