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Prompt tuning, which only tunes continuous prompts with a frozen languagemodel, substantially reduces per-task storage and memory usage at training.However, in the context of NLU, prior work reveals that prompt tuning does notperform well for normal-sized pretrained models. We also find that existingmethods of prompt tuning cannot handle hard sequence labeling tasks, indicatinga lack of universality. We present a novel empirical finding that properlyoptimized prompt tuning can be universally effective across a wide range ofmodel scales and NLU tasks. It matches the performance of finetuning whilehaving only 0.1%-3% tuned parameters. Our method P-Tuning v2 is animplementation of Deep Prompt Tuning \cite{li2021prefix,qin2021learning}optimized and adapted for NLU. Given the universality and simplicity ofP-Tuning v2, we believe it can serve as an alternative to finetuning and astrong baseline for future research.Our code and data are released athttps://github.com/THUDM/P-tuning-v2.

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks