Original Paper: https://arxiv.org/abs/2311.05661

By: Qinyuan Ye, Maxamed Axmed, Reid Pryzant, Fereshte Khani

Abstract:

Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models on customized tasks. It requires complex reasoning to examine the model's errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that large language models can be meta-prompted to perform automatic prompt engineering, we argue that their potential is limited due to insufficient guidance for complex reasoning in the meta-prompt. We fill this gap by infusing into the meta-prompt three key components: detailed descriptions, context specification, and a step-by-step reasoning template. The resulting method, named PE2, showcases remarkable versatility across diverse language tasks. It finds prompts that outperform "let's think step by step" by 6.3% on MultiArith and 3.1% on GSM8K, and outperforms competitive baselines on counterfactual tasks by 6.9%. Further, we show that PE2 can make targeted prompt edits, rectify erroneous prompts, and induce multi-step plans for complex tasks.

Summary Notes

Enhancing Large Language Models with PE2: A Practical Guide for AI Engineers

As artificial intelligence continues to advance, the role of prompt engineering has become increasingly vital for improving the performance of large language models (LLMs) on complex tasks.

Crafting effective prompts is a nuanced task that many AI engineers find challenging. This post introduces the PE2 methodology, offering a straightforward approach to refine prompt engineering for better model outcomes.

Introduction to Prompt Engineering

The recent strides in AI can largely be attributed to LLMs' ability to mimic human-like text. The effectiveness of these models largely depends on the quality of prompts they receive. Prompt engineering is about creating these prompts to clearly convey tasks to models. Despite its significance, devising effective prompts for complex tasks is a daunting challenge for AI engineers.

Understanding PE2 Methodology

PE2 represents a breakthrough in prompt engineering, offering a systematic way to craft meta-prompts that improve the tuning of LLMs.

Here's an overview of what PE2 involves:

• Two-step Task Description: Simplifies the prompt engineering process, detailing the necessary steps for models to grasp and perform tasks.
• Context Specification: Outlines how to present prompts and input text, giving the model a clear context.
• Step-by-step Reasoning Template: Helps the model through a structured reasoning path, making it easier to spot errors and refine prompts.

Experimental Validation

Experiments with top-tier models like GPT-4, involving tasks like mathematical reasoning and instruction following, have proven PE2's effectiveness, showing it outperforms traditional methods, especially in complex reasoning tasks.

Results and Insights

The findings from these experiments highlight PE2's capacity to boost model performance:

• Performance Improvement: Complex reasoning tasks saw a performance increase of over 6% with PE2.
• Prompt Refinement: PE2 excels at improving prompts through a comprehensive feedback and reasoning process.

Analysis and Observations

PE2 improved prompt generation's stability and efficiency and underscored the value of precise prompt adjustments for better task performance.

Its ability to be applied across different models and tasks suggests exciting possibilities for future research and applications.

Future Challenges and Directions

PE2 opens the door to several promising research areas, including:

• Dynamic Meta-Prompt Structures: Exploring more flexible and reactive meta-prompt designs.
• Model Generalization: Extending PE2's use to a wider array of LLMs and tasks.
• Automatic Model Tuning: Applying PE2 principles to broader model optimization efforts.

Conclusion: PE2's Role in Advancing AI Engineering

PE2 stands as a significant advancement in AI, providing a solid framework to improve LLMs' reasoning and adaptability. For AI engineers in enterprise settings, adopting PE2 could bridge the gap to achieving exceptional results.

As AI continues to evolve, PE2's methodologies will likely be instrumental in the ongoing refinement of prompt engineering.

In essence, while the journey to perfect prompt engineering is far from over, tools like PE2 equip AI engineers to navigate the complexities of human language more effectively, unlocking unprecedented levels of model performance and capability.