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

Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training. It matches the performance of finetuning while having only 0.1%-3% tuned parameters.

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• 1 However, in the context of NLU, prior work reveals that prompt tuning does not perform well for normal-sized pretrained models.
• 2 We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of universality.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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