  The wayward quality of continuous prompts stresses the importance of their
interpretability as unexpected and unpredictable behaviors appear following
training, especially in the context of large language models automating
people-sensitive tasks such as resume screening. In this paper we present a
novel method of constructing continuous prompts via discrete prompt embeddings
and evaluate improvements to continuous prompt interpretability and inference
accuracy. For a set of manually designed discrete prompts $\mathcal{D}$, which
we tokenize and embed each into tensor form, we train a model to predict the
weights such that the linear combinations of those prompts correspond to higher
performance on natural language understanding tasks.
