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arXiv · 2610.05717

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

Yilun Zhu·Naihao Deng·Yingcong Li·Naichen Shi·Clayton Scott
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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.

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Yilun Zhu, Naihao Deng, Yingcong Li, Naichen Shi, Clayton Scott. 2026-10-05. Retrieval-Based In-Context Learning: A Domain Adaptation Framework. https://arxiv.org/abs/2610.05717

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