Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering

Derek Austin, Elliott Chartock

TL;DR

GRAD-SUM addresses the problem of manually crafting prompts for LLMs by introducing a gradient-based automatic prompt optimization loop that incorporates task descriptions and user-defined evaluation criteria. The framework comprises generation, evaluation, gradients, gradient summarization, and prompt editing, all guided by a beam search and LLM-as-a-judge feedback. Empirical results across multiple benchmarks including GSM8K, Orca Math, Neural Bridge RAG, HellaSwag, HotPot QA, MMLU, and MT/Vicuna Bench show consistent improvements over initial prompts and a strong advantage over DSPY, with average gains around 14%. The work demonstrates a scalable, flexible approach to auto-prompt tuning that can operate without an explicit ground-truth answer and highlights gradient summarization as key to generalization and cost control.

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.

GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering

TL;DR

GRAD-SUM addresses the problem of manually crafting prompts for LLMs by introducing a gradient-based automatic prompt optimization loop that incorporates task descriptions and user-defined evaluation criteria. The framework comprises generation, evaluation, gradients, gradient summarization, and prompt editing, all guided by a beam search and LLM-as-a-judge feedback. Empirical results across multiple benchmarks including GSM8K, Orca Math, Neural Bridge RAG, HellaSwag, HotPot QA, MMLU, and MT/Vicuna Bench show consistent improvements over initial prompts and a strong advantage over DSPY, with average gains around 14%. The work demonstrates a scalable, flexible approach to auto-prompt tuning that can operate without an explicit ground-truth answer and highlights gradient summarization as key to generalization and cost control.

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.
Paper Structure (20 sections, 2 figures, 2 tables)

This paper contains 20 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Generation
  5. Evaluation
  6. Gradients
  7. Gradient Summarization
  8. Prompt Editor
  9. Experiments & Results
  10. Gradient Summarization
  11. Conclusion
  12. Limitations
  13. Prompt Optimization Results
  14. GSM8k
  15. Orca Math
  16. ...and 5 more sections

Figures (2)

  • Figure 1: An illustration of one GRAD-SUM training loop. Modules are sequential starting with generation. The prompt chosen in our prompt editor model is then fed back to the generation module and the training loop restarts.
  • Figure 2: Our gradient summarization approach outperforms no gradient summarization by 5%.