KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning 事件

KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning arXiv:2509.15676v2 Announce Type: replace-cross Abstract: In-context learning (ICL) has emerged as a powerful paradigm for adapting large language models (LLMs) to new and data-scarce tasks using only a few carefully selected task-specific examples presented in the prompt. However, given the limited context size of LLMs, a fundamental question arises: Which examples should be selected to maximize performance on a given