  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.
