EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

TL;DR

EvoPrompt introduces an evolutionary algorithm-based framework that automates prompt optimization for large language models, significantly improving performance across diverse tasks without requiring gradient information.

Contribution

This paper presents EvoPrompt, a novel method connecting LLMs with evolutionary algorithms for discrete prompt optimization, demonstrating superior results over existing automatic prompt generation techniques.

Findings

EvoPrompt outperforms human-crafted prompts and existing methods by up to 25% on BBH tasks.

The approach effectively optimizes prompts for both closed- and open-source LLMs across 31 datasets.

Connecting LLMs with EAs creates synergistic benefits for prompt engineering.

Abstract

Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EvoPrompt, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, we connect LLMs with EAs. This approach allows us to simultaneously leverage the powerful language processing capabilities of LLMs and the efficient optimization performance of EAs. Specifically, abstaining from any gradients or parameters, EvoPrompt starts from a population of prompts and iteratively generates new prompts with LLMs based on the evolutionary operators, improving the population based on the development set. We optimize prompts for both closed- and open-source LLMs including GPT-3.5 and Alpaca, on 31 datasets covering language understanding, generation tasks, as well as BIG-Bench Hard (BBH) tasks. EvoPrompt significantly outperforms human-engineered prompts and existing methods for automatic prompt generation (e.g., up to 25% on BBH). Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs creates synergies, which could inspire further research on the combination of LLMs and conventional algorithms.