The content discusses a framework for efficiently selecting prompts for generative language models, such as GPT, to generate desired outputs. The key points are:
Prompt selection is crucial for effectively leveraging generative language models, especially for smaller enterprises and non-profit organizations with limited resources for model development.
The authors reformulate the prompt selection problem as a simulation optimization problem, where each prompt evaluation through the language model is considered a simulation sample.
The framework consists of two stages:
The authors also propose a refinement procedure to further improve the prompt selection by constructing a projection mapping from the high-dimensional latent space to the moderate-dimensional subspace.
Numerical experiments demonstrate the effectiveness of the proposed framework, showing the superiority of Bayesian neural networks as surrogate models and the efficiency of the probabilistic reparameterization for optimizing the acquisition function.
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