arxiv Preprint – Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

In this episode we discuss Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
by Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel. The paper presents PROMPTBREEDER, a method for evolving and adapting prompts for Large Language Models (LLMs) in order to enhance their reasoning abilities. It uses an LLM to mutate a population of task-prompts and evaluates their fitness on a training set. The mutation of task-prompts is guided by self-improved mutation-prompts generated by the LLM, leading to improved performance in tasks such as arithmetic, commonsense reasoning, and hate speech classification.