Year: 2026
Type: conference-paper
Abstract: In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set.We propose Unseen Coverage Selection (UCS), a training-free, subset-level coverage prior motivated by the principle that a good demonstration set should expose the model to latent cluster unrevealed by the currently selected subset.UCS operationalizes this idea by (1) inducing discrete latent clusters from model-consistent embeddings and (2) estimating the number of unrevealed clusters within a candidate subset via a Smoothed Good-Turing estimator from its empirical frequency spectrum.Unlike previous selection methods, UCS is coverage-based and training-free, and can be seamlessly combined with both query-dependent and queryindependent selection baselines via a simple regularized objective.Experiments on multiple intent-classification and reasoning benchmarks with frontier LLMs show that augmenting strong baselines with UCS consistently improves ICL accuracy by up to 2-6% under the same selection budget, while also yielding insights into task-and model-level latent cluster distributions.
Institution: University of Pennsylvania
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166873359