Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution
Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel
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, evaluates them for fitness on a training set, and repeats this process over multiple generations to evolve task-prompts. 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 mutation-prompts 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.
BibTeX
@inproceedings{
fernando2024promptbreeder,
title={Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution},
author={Chrisantha Fernando and Dylan Sunil Banarse and Henryk Michalewski and Simon Osindero and Tim Rockt{\"a}schel},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=9ZxnPZGmPU}
}