PRewrite: Prompt Rewriting with Reinforcement Learning
Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky · 2024
Prompt engineering is critical for the development of LLM-based applications.However, it is usually done manually in a "trial and error" fashion that can be time consuming, ineffective, and sub-optimal.Even for the prompts which seemingly work well, there is always a lingering question: can the prompts be made better with further modifications?To address these problems, we investigate automated prompt engineering in this paper.Specifically, we propose PRewrite, an automated method to rewrite an under-optimized prompt to a more effective prompt.We instantiate the prompt rewriter using an LLM.The rewriter LLM is trained using reinforcement learning to optimize the performance on a given downstream task.We conduct experiments on diverse benchmark datasets, which demonstrates the effectiveness of PRewrite.