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cs.LG2026
Analyzing the Effect of Noise in LLM Fine-tuning
Lingfang Li, Procheta Sen
Fine-tuning is the dominant paradigm for adapting pretrained large language models (LLMs) to downstream NLP tasks. In practice, fine-tuning datasets may contain various forms of no…
cs.LG2025
Label Smoothing Improves Gradient Ascent in LLM Unlearning
Zirui Pang, Hao Zheng, Zhijie Deng +3
LLM unlearning has emerged as a promising approach, aiming to enable models to forget hazardous/undesired knowledge at low cost while preserving as much model utility as possible.…
cs.LG2025
SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing
Qiuhao Liu, Ling Li, Yao Lu +3
Deep neural networks tend to memorize noisy labels, severely degrading their generalization performance. Although Mixup has demonstrated effectiveness in improving generalization a…