PRewrite: Prompt Rewriting with Reinforcement Learning
CoRR(2024)
摘要
Prompt engineering is critical for the development of LLM-based applications.
However, it is usually done manually in a "trial and error" fashion. This
manual procedure can be time consuming, ineffective, and the generated prompts
are, in a lot of cases, 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 questions, in this paper, we investigate prompt engineering
automation. We consider a specific use case scenario in which developers/users
have drafted initial prompts, but lack the time/expertise to optimize them. We
propose PRewrite, an automated tool to rewrite these drafts and to generate
highly effective new prompts. PRewrite is based on the Reinforcement Learning
(RL) framework which allows for end-to-end optimization and our design allows
the RL search to happen in a large action space. The automated tool leverages
manually crafted prompts as starting points which makes the rewriting procedure
more guided and efficient. The generated prompts are human readable, and
self-explanatory, unlike some of those in previous works. We conducted
extensive experiments on diverse datasets and found that the prompts generated
with this new method not only outperform professionally crafted prompts, but
also prompts generated with other previously proposed methods.
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