  Soft prompt tuning achieves superior performances across a wide range of
few-shot tasks. However, the performances of prompt tuning can be highly
sensitive to the initialization of the prompts. We also empirically observe
that conventional prompt tuning methods cannot encode and learn sufficient
task-relevant information from prompt tokens. In this work, we develop an
information-theoretic framework that formulates soft prompt tuning as
maximizing mutual information between prompts and other model parameters (or
encoded representations). This novel view helps us to develop a more efficient,
accurate and robust soft prompt tuning method InfoPrompt. With this framework,
we develop two novel mutual information based loss functions, to (i) discover
proper prompt initialization for the downstream tasks and learn sufficient
task-relevant information from prompt tokens and (ii) encourage the output
representation from the pretrained language model to be more aware of the
task-relevant information captured in the learnt prompt. Extensive experiments
validate that InfoPrompt can significantly accelerate the convergence of the
prompt tuning and outperform traditional prompt tuning methods. Finally, we
provide a formal theoretical result for showing to show that gradient descent
type algorithm can be used to train our mutual information loss.
