AINeutralarXiv – CS AI · 9h ago6/10
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KITE: Kernelized and Information Theoretic Exemplars for In-Context Learning
Researchers introduce KITE, a novel example selection method for in-context learning in large language models that uses information theory and kernel methods to choose task-specific examples from a prompt bank. The approach addresses limitations of existing nearest-neighbor methods by improving diversity and generalization, demonstrating measurable improvements across classification tasks in label-scarce scenarios.