Summary of Promptwizard: Task-aware Prompt Optimization Framework, by Eshaan Agarwal et al.
PromptWizard: Task-Aware Prompt Optimization Framework
by Eshaan Agarwal, Joykirat Singh, Vivek Dani, Raghav Magazine, Tanuja Ganu, Akshay Nambi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 In this paper, researchers introduce PromptWizard, a novel framework for automatically optimizing prompts for large language models (LLMs). The framework uses a self-evolving mechanism to iteratively refine both prompt instructions and in-context examples, leading to superior performance across 45 tasks. This approach is particularly effective even with limited training data, smaller LLMs, and various architectures. Additionally, PromptWizard reduces the cost of API calls, token usage, and overall expense. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PromptWizard is a new way to help large language models understand what to do. Right now, people have to manually come up with instructions for these models, which can be hard and time-consuming. This paper shows how PromptWizard can automatically create good instructions by refining them through a process of testing and improvement. It works well even when there’s not much data or the model is smaller, and it saves money compared to other ways of optimizing prompts. |
Keywords
» Artificial intelligence » Prompt » Token
