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arXiv · 2205.12309

Structured Prompt Tuning

Chi-Liang Liu·Hung-yi Lee·Wen-tau Yih
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Abstract

We propose structured prompt tuning, a simple and effective method to improve prompt tuning. Instead of prepending a sequence of tunable embeddings to the input, we generate the soft prompt embeddings through a hypernetwork. Our approach subsumes the standard prompt tuning, allows more flexibility in model design and can be applied to both single-task and multi-task training settings. Empirically, structured prompt tuning shows a gain of +1.2$~1.5 points on the GLUE benchmark and is less sensitive to the change of learning rate, compared to standard prompt tuning.

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Chi-Liang Liu, Hung-yi Lee, Wen-tau Yih. 2022-05-24. Structured Prompt Tuning. https://arxiv.org/abs/2205.12309

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