# Structured Prompt Tuning

**Authors:** Chi-Liang Liu, Hung-yi Lee, Wen-tau Yih  
**arXiv:** [2205.12309](https://arxiv.org/abs/2205.12309)  
**Date:** 2022-05-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2205.12309v1/

## 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.