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cs.LG2025
DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration
Tianteng Gu, Bei Liu, Bo Xiao +3
Pruning is a widely used technique to compress large language models (LLMs) by removing unimportant weights, but it often suffers from significant performance degradation - especia…
cs.LG2025
DataDecide: How to Predict Best Pretraining Data with Small Experiments
Ian Magnusson, Nguyen Tai, Ben Bogin +10
Because large language models are expensive to pretrain on different datasets, using smaller-scale experiments to decide on data is crucial for reducing costs. Which benchmarks and…
cs.LG2025★ 1 cited
Efficient Test-Time Scaling via Self-Calibration
Chengsong Huang, Langlin Huang, Jixuan Leng +2
Increasing test-time computation is a straightforward approach to enhancing the quality of responses in Large Language Models (LLMs). While Best-of-N sampling and Self-Consistency…