Summary of Prompt Exploration with Prompt Regression, by Michael Feffer et al.
Prompt Exploration with Prompt Regression
by Michael Feffer, Ronald Xu, Yuekai Sun, Mikhail Yurochkin
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed framework, Prompt Exploration with Prompt Regression (PEPR), aims to systematize large language model (LLM) prompt creation and selection processes by predicting the effect of prompt combinations given results for individual prompt elements. This approach fills a gap in prior works that mostly focus on searching the space of prompts without considering relationships between prompt variations. The framework is evaluated using open-source LLMs of different sizes on various tasks, showcasing its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be tricky to use, but a new method makes it easier! Researchers developed a system called PEPR that helps pick the best prompts for a task. This is important because before, people just tried lots of prompts until one worked. The PEPR method uses previous results to predict what will happen when you combine different prompts. It’s like trying to figure out which ingredients will make the best cake recipe! The team tested their approach with different-sized language models and tasks, showing that it really works. |
Keywords
» Artificial intelligence » Large language model » Prompt » Regression
