EASE-Prompt-Optimization
What it is
EASE is an automated exemplar selection method that optimizes the set of examples provided in-context learning (ICL). It utilizes a neural bandit algorithm to select and order exemplars efficiently to improve model performance.
The problem it solves
Manual selection of exemplars for in-context learning is time-consuming and often fails to account for the impact of example ordering.
What you could use it for
- Optimizing in-context learning for Large Language Models (LLMs)
- Automated selection of few-shot examples for diverse tasks
- Improving prompt performance by accounting for the order of examples
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.