PROPANE: Prompt design as an inverse problem
Rimon Melamed Lucas H. McCabe Tanay Wakhare Yejin Kim H. Howie Huang Enric Boix-Adsera
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.