Summary of Grad-sum: Leveraging Gradient Summarization For Optimal Prompt Engineering, by Derek Austin et al.
GRAD-SUM: Leveraging Gradient Summarization for Optimal Prompt Engineering
by Derek Austin, Elliott Chartock
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces GRAD-SUM, a scalable method for automating large language model (LLM) prompt engineering. Prompt engineering is typically a manual process involving iterative generation, evaluation, and refinement of prompts to achieve high-quality outputs. Existing methods often require tuning to specific tasks with given answers or are costly. GRAD-SUM builds on gradient-based optimization techniques, incorporating user-defined task descriptions and evaluation criteria, as well as a novel gradient summarization module for effective feedback generalization. The approach consistently outperforms existing methods across various benchmarks, showcasing its versatility and effectiveness in automatic prompt optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GRAD-SUM is a new way to help large language models get the right information from us. Usually, we have to spend a lot of time coming up with good questions or prompts for these models, but GRAD-SUM makes it easier by using special techniques to figure out what works best. It can take into account what we want the model to do and how well it’s doing, making it more effective than other methods. |
Keywords
» Artificial intelligence » Generalization » Large language model » Optimization » Prompt » Summarization
