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This paper proposes a submodular coreset selection method for LLM benchmarks that selects a subset of prompts without using model evaluation outcomes, achieving score preservation across 35 benchmarks and 18 LLMs.
Proves a tight approximation ratio for the greedy algorithm in myopic Bayesian active learning for linear regression, identifying the maximum initial leverage score as a key quantity.