SEED models data selection as Weighted Independent Set on a similarity graph, using node value calibration and local scale normalization to produce compact high-quality training subsets that outperform prior methods on instruction tuning and segmentation tasks.
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SEED: Targeted Data Selection by Weighted Independent Set
SEED models data selection as Weighted Independent Set on a similarity graph, using node value calibration and local scale normalization to produce compact high-quality training subsets that outperform prior methods on instruction tuning and segmentation tasks.