Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Chrisantha Fernando; Dylan Banarse; Henryk Michalewski; Simon Osindero; Tim Rocktäschel

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arXiv · 2309.16797

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Chrisantha Fernando·Dylan Banarse·Henryk Michalewski·Simon Osindero·Tim Rocktäschel

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Abstract

Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.

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Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel. 2023-09-28. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution. https://arxiv.org/abs/2309.16797

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