  Prompt engineering, as an efficient and effective way to leverage Large
Language Models (LLM), has drawn a lot of attention from the research
community. The existing research primarily emphasizes the importance of
adapting prompts to specific tasks, rather than specific LLMs. However, a good
prompt is not solely defined by its wording, but also binds to the nature of
the LLM in question. In this work, we first quantitatively demonstrate that
different prompts should be adapted to different LLMs to enhance their
capabilities across various downstream tasks in NLP. Then we novelly propose a
model-adaptive prompt optimizer (MAPO) method that optimizes the original
prompts for each specific LLM in downstream tasks. Extensive experiments
indicate that the proposed method can effectively refine prompts for an LLM,
leading to significant improvements over various downstream tasks.
