Retrieval-Based In-Context Learning: A Domain Adaptation Framework

Abstract:In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based on similarity to the query, rather than sampled independently. In this work, we formulate ICR as a type of domain adaptation problem, where the source distribution $P$ of the database may differ from the target distribution $Q$ of the test query-label pair. We investigate the performance of ICR under a flexible class of distributional shifts that substantially extends prior work \citep{li2024fine,guo2025retrieval}, and establish theoretical guarantees that quantify the benefits and pitfalls of this learning paradigm. Our theory is verified by experiments on synthetic and language tasks.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.05717 [stat.ML]

(or arXiv:2610.05717v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.05717 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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