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This paper introduces an embedding-based subset selection method using Whisper's pretrained encoder and a greedy k-center algorithm to pick diverse training subsets for ASR fine-tuning. Evaluated on Hebrew and Danish, it shows k-center selection beats random sampling, especially at small subset sizes, due to greater lexical diversity.
This paper introduces Metric Match, a method for selecting a subset of samples for human annotation to estimate LLM judge reliability more efficiently, reducing annotation costs by 32.5% and achieving a win-rate of 0.838 against random selection.
GRASP introduces a geometry-aware, interaction-based method for scalable pretraining data attribution that models subset dynamics, outperforming existing additive approaches by over double the task-level rank correlation while reducing computation costs.