P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
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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 reveals that prompt tuning does not perform well for normal-sized pre-trained models. We also find that existing methods of prompt tuning cannot handle hard sequence tagging tasks, indicating a 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. 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 <cit.> optimized and adapted for NLU. Given the universality and simplicity of P-Tuning v2, we believe it can serve as an alternative to fine-tuning and a strong baseline for future research.
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