Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such prompts via manual rewriting or gradient-based tuning. However, manual rewriting is time-consuming and requires subjective interpretation, while gradient-based tuning can be extremely computationally demanding for large models and requires full access to model weights, which may not be available for API-based models. In this work, we introduce Gradient-free Instructional Prompt Search (GrIPS), a gradient-free, edit-based search approach for improving task instructions for large language models. GrIPS takes in instructions designed for humans and automatically returns an improved, edited prompt, while allowing for API-based tuning. The instructions in our search are iteratively edited using four operations (delete, add, swap, paraphrase) on text at the phrase-level. With InstructGPT models, GrIPS improves the average task performance by up to 4.30 percentage points on eight classification tasks from the Natural-Instructions dataset. We see improvements for both instruction-only prompts and for k-shot example+instruction prompts. Notably, GrIPS outperforms manual rewriting following the guidelines in Mishra et al. (2022) and also outperforms purely example-based prompts while controlling for the available compute and data budget. Lastly, we provide qualitative analysis of the edited instructions across several scales of GPT models. Our code is available at: https://github.com/archiki/GrIPS

 GrIPS 是一种基于编辑的、无梯度搜索方法，用于自动改进针对大型语言模型的任务指令，可以在 API 基础上进行调整，具有较高的效率，可显著提高大型语言模型在分类任务上的性能。

GrIPS：无梯度、基于编辑的指令搜索，用于提示大型语言模型