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[2309.16797] Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

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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.

[2309.16797] Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Computation and Language arXiv:2309.16797 (cs) [Submitted on 28 Sep 2023] Title: Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Authors: Chrisantha Fernando , Dylan Banarse , Henryk Michalewski , Simon Osindero , Tim Rocktäschel View a PDF of the paper titled Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution, b

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