Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval

This paper introduces DOPA, a demonstration search framework for robust in-context learning with Large Language Models (LLMs). DOPA uses an OOD proxy to approximate inaccessible target domains and a Mahalanobis distance-based global diversity constraint for demonstration retrieval.