Structured Prompt Tuning

• URL: http://arxiv.org/abs/2205.12309v1
• Date: Tue, 24 May 2022 18:36:34 GMT
• Title: Structured Prompt Tuning
• Authors: Chi-Liang Liu, Hung-yi Lee, Wen-tau Yih
• Abstract summary: 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.
• Score: 83.71253868369999
• License: http://creativecommons.org/licenses/by/4.0/
• 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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