With the advancement in generative language models, the selection of prompts
has gained significant attention in recent years. A prompt is an instruction or
description provided by the user, serving as a guide for the generative
language model in content generation. Despite existing methods for prompt
selection that are based on human labor, we consider facilitating this
selection through simulation optimization, aiming to maximize a pre-defined
score for the selected prompt. Specifically, we propose a two-stage framework.
In the first stage, we determine a feasible set of prompts in sufficient
numbers, where each prompt is represented by a moderate-dimensional vector. In
the subsequent stage for evaluation and selection, we construct a surrogate
model of the score regarding the moderate-dimensional vectors that represent
the prompts. We propose sequentially selecting the prompt for evaluation based
on this constructed surrogate model. We prove the consistency of the sequential
evaluation procedure in our framework. We also conduct numerical experiments to
demonstrate the efficacy of our proposed framework, providing practical
instructions for implementation.

基于模拟优化，提出了一个两阶段框架以实现生成语言模型的提示选择，该框架能够在评估和选择阶段中构建代表提示的中等维度向量的得分模型，并证明了该框架中的顺序评估过程的一致性。数值实验证明了此框架的有效性，为实施提供了实际指导。