#coreset-selection News & Analysis

3 articles tagged with #coreset-selection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Jun 237/10

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Learning More from Less: Unlocking Internal Representations for Benchmark Compression

RepCore, a new method for compressing LLM benchmarks, uses aligned hidden states from neural networks to identify representative test subsets rather than relying solely on correctness labels. The approach achieves accurate performance estimation with as few as ten source models, addressing the statistical instability that plagues existing coreset methods when evaluation data is limited.

AIBullisharXiv – CS AI · Mar 47/103

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FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection

Researchers propose FAST, a new DNN-free framework for coreset selection that compresses large datasets into representative subsets for training deep neural networks. The method uses frequency-domain distribution matching and achieves 9.12% average accuracy improvement while reducing power consumption by 96.57% compared to existing methods.

AINeutralarXiv – CS AI · Apr 156/10

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GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization

Researchers propose GRACE, a dynamic coreset selection framework that reduces LLM training costs by intelligently selecting representative dataset subsets. The method combines representation diversity with gradient-based metrics and uses k-NN graph propagation to adapt to evolving training dynamics, demonstrating improved efficiency across multiple benchmarks.