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.

翻译：连续提示的不可预测性凸显了其可解释性的重要性，尤其是在大型语言模型自动化执行简历筛选等涉及人员敏感任务时，训练后出现的意外和不可预测行为更需关注。本文提出一种通过离散提示嵌入构建连续提示的新方法，并评估了该方法在提升连续提示可解释性和推理准确性方面的改进。针对一组人工设计的离散提示 $\mathcal{D}$，我们将其分词并嵌入为张量形式后，训练一个模型来预测权重，使得这些提示的线性组合在自然语言理解任务中表现出更优性能。