A gradient-based greedy selection method (SELECT) recovers effective substitute fine-tuning data from two language model checkpoints, approaching the original model's performance on classification and SFT tasks.
Coresets via bilevel optimization for continual learning and streaming
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Approximating Language Model Training Data from Weights
A gradient-based greedy selection method (SELECT) recovers effective substitute fine-tuning data from two language model checkpoints, approaching the original model's performance on classification and SFT tasks.