Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Original Paper: https://arxiv.org/abs/2309.08532

By: Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang

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.

Summary Notes

Automating Prompt Design with EvoPrompt: A Game-Changer for AI

In the dynamic world of AI and machine learning, Large Language Models (LLMs) are pivotal in natural language processing (NLP) tasks. Yet, the effectiveness of these models often depends on the intricate process of prompt design.
To streamline this, the innovative EvoPrompt framework has been introduced, merging the power of LLMs with evolutionary algorithms (EAs) to automate prompt design. Here, we explore how EvoPrompt is transforming the prompt design process, proving invaluable for AI professionals in various industries.

The EvoPrompt Framework: Revolutionizing Prompt Design

EvoPrompt marks a significant shift in prompt design by starting with initial prompts and refining them through evolutionary principles, using LLMs. This method not only achieves remarkable results across a wide range of NLP tasks but does so without requiring direct access to the model's parameters or gradients. Highlights include:

• Parameter and Gradient Independence: EvoPrompt excels in generating effective and readable prompts without needing model internals.
• Versatility Across NLP Tasks: Demonstrated success on 31 different datasets shows EvoPrompt's broad applicability and superior performance.

Methodology: Leveraging Evolutionary Algorithms

At the heart of EvoPrompt is its innovative use of Genetic Algorithms (GA) and Differential Evolution (DE) to evolve prompts. These algorithms guide the creation of new prompts through evolutionary operations like mutation and crossover, directly instructed to LLMs, enabling:

• Evolutionary Operations for Prompt Generation: Through LLM-guided mutations and crossovers, EvoPrompt efficiently produces tailored prompts.
• Compatibility With Diverse Tasks: The framework's robustness is evident across various NLP tasks, showcasing its wide-ranging effectiveness.

Performance: Outshining Traditional Methods

EvoPrompt's performance has been thoroughly evaluated, showing its superiority over manual and other automatic prompt generation methods in tasks ranging from language understanding to the challenging BIG-Bench Hard tasks. Key findings include:

• Flexibility: EvoPrompt's ability to adapt different evolutionary algorithms for prompt optimization suggests potential for further innovative applications.

Insights: The Mechanics Behind EvoPrompt

Analysis of EvoPrompt uncovers the importance of selection strategies, mutation operations, and the quality of the initial prompt set. The use of evolutionary algorithms is crucial in exploring prompt options and avoiding suboptimal solutions, revealing:

• Prompt Diversity and Optimization: The variety of prompts generated and the optimization process highlight EvoPrompt's novel approach to prompt design.

Looking Ahead: The Future of Prompt Design Automation

EvoPrompt introduces a groundbreaking method for automating prompt design, blending LLMs' capabilities with the optimization power of evolutionary algorithms.

This not only boosts the efficiency and effectiveness of prompt design but also paves the way for new AI applications. Future prospects for EvoPrompt and similar technologies in advancing AI are vast and promising.

Implementation Insights

For those keen on deploying EvoPrompt, the framework details specific algorithms for GA and DE, experimental setups, and an analysis of optimization costs. Highlights include:

• Performance Insights: Comparative analyses affirm EvoPrompt's effectiveness across various tasks and datasets.
• Showcase of Optimal Prompts: Examples of optimized prompts for different tasks illustrate the practical benefits of the framework.

EvoPrompt stands as a pioneering solution in prompt design automation, offering AI engineers a robust tool to leverage LLMs more effectively. The ongoing integration of LLMs with evolutionary algorithms opens up endless possibilities for AI innovation and advancement.