GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering

• URL: http://arxiv.org/abs/2407.12865v1
• Date: Fri, 12 Jul 2024 19:11:21 GMT
• Title: GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering
• Authors: Derek Austin, Elliott Chartock,
• Abstract summary: We introduce GRAD-SUM, a scalable and flexible method for automatic prompt engineering. Our approach incorporates user-defined task descriptions and evaluation criteria, and features a novel gradient summarization module. Our results demonstrate that GRAD-SUM consistently outperforms existing methods across various benchmarks.
• Score: 0.2877502288155167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt engineering for large language models (LLMs) is often a manual time-intensive process that involves generating, evaluating, and refining prompts iteratively to ensure high-quality outputs. While there has been work on automating prompt engineering, the solutions generally are either tuned to specific tasks with given answers or are quite costly. We introduce GRAD-SUM, a scalable and flexible method for automatic prompt engineering that builds on gradient-based optimization techniques. Our approach incorporates user-defined task descriptions and evaluation criteria, and features a novel gradient summarization module to generalize feedback effectively. Our results demonstrate that GRAD-SUM consistently outperforms existing methods across various benchmarks, highlighting its versatility and effectiveness in automatic prompt optimization.

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