HARP is a train-based data selector for LLM finetuning that uses hierarchical active region pruning and empirical Bayes posteriors to achieve up to 8.9 point gains with roughly 7 times fewer training examples.
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric
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HARP: Efficient Data Selection for Finetuning Large Language Models
HARP is a train-based data selector for LLM finetuning that uses hierarchical active region pruning and empirical Bayes posteriors to achieve up to 8.9 point gains with roughly 7 times fewer training examples.