Optimal-Demo-Selection-ICL
What it is
A research-grade framework for benchmarking and evaluating demonstration selection strategies in In-Context Learning (ICL). It implements multiple state-of-the-art algorithms (like IDS, RDES, and Se²) to analyze how example relevance, diversity, and ordering impact LLM performance.
The problem it solves
The lack of a unified, modular framework to rigorously evaluate and compare different demonstration selection strategies for In-Context Learning.
What you could use it for
- Benchmarking different few-shot demonstration selection algorithms
- Evaluating the impact of example diversity on LLM reasoning tasks
- Reproducing research on optimal demonstration selection for In-Context Learning
- Analyzing the trade-off between computational cost and accuracy in ICL
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.