Mingbo Yang

Performance-Driven Demonstration Selection for In-Context Learning

In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks with considerable performance gains, yet its effectiveness is highly sensitive to the choice of demonstrations. Most existing selection methods rely on heuristic or proxy signals (e.g., similarity, diversity, or unc