Carefully-designed prompts are key to inducing desired behavior in Large Language Models (LLMs). As a result, great effort has been dedicated to engineering prompts that guide LLMs toward particular behaviors. In this work, we propose an automatic prompt optimization framework, PROPANE, which aims to find a prompt that induces semantically similar outputs to a fixed set of examples without user intervention. We further demonstrate that PROPANE can be used to (a) improve existing prompts, and (b) discover semantically obfuscated prompts that transfer between models.

翻译：精心设计的提示对于引导大型语言模型（LLMs）产生预期行为至关重要。因此，大量研究工作致力于设计能够驱动LLMs表现出特定行为的提示。本文提出了一种自动化提示优化框架PROPANE，其目标是在无需用户干预的情况下，找到能够使模型输出与固定示例集在语义上相似的提示。我们进一步证明，PROPANE可用于（a）改进现有提示，以及（b）发现可在模型间迁移的语义混淆提示。