AIBullisharXiv – CS AI · Apr 107/10
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SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training
Researchers introduce SPICE, a data selection algorithm that reduces large language model training data requirements by 90% while maintaining performance by identifying and minimizing gradient conflicts between training samples. The method combines information-theoretic principles with practical efficiency improvements, enabling effective model tuning on just 10% of typical datasets across multiple benchmarks.