Data Efficient Sample Selection for In-Context Learning

• URL: http://arxiv.org/abs/2609.06670v2
• Date: Sun, 13 Sep 2026 07:50:02 GMT
• Title: Data Efficient Sample Selection for In-Context Learning
• Abstract summary: In-context learning (ICL) aids large language models (LLMs) to adapt to new tasks without need for fine-tuning.<n>Existing approaches for selection do not model the complex relationship between ICL samples and downstream LLM performance.<n>We introduce DearICL, a new framework that models demonstration example selection as a subset ranking problem.
• Score: 7.910984819642885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The In-context learning (ICL) paradigm aids large language models (LLMs) to adapt to new tasks without need for fine-tuning. However, selecting an optimal combination of demonstration examples from a large pool of example subsets is a challenging problem. Existing approaches for selection do not model the complex relationship between ICL samples and downstream LLM performance. They typically perform static task-level selection, choosing subsets once offline, which can fail to generalize to unseen queries. We introduce DearICL (Data Efficient Algorithm for Ranking) ICL samples, a new framework that models demonstration example selection as a subset ranking problem. DearICL employs a non-linear surrogate employing a differentiable sorting objective within a gap-index bandit algorithm. The gap-index based approach enables fine-grained separation of good arms and borderline arms, which is used as an auxiliary objective to train the non-linear surrogate through sufficient sampling of borderline arms, supporting instance-level subset ranking. On exemplar selection benchmarks with open-source LLMs, DearICL achieves 8.08-15.9% accuracy gains over strong linear bandit baselines, with low sample complexity. Code and data: https://github.com/VenkteshV/DearICL.

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