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cs.LG2026
CAP: Controllable Alignment Prompting for Unlearning in LLMs
Zhaokun Wang, Jinyu Guo, Jingwen Pu +7
Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance a…
cs.LG2025
GateRA: Token-Aware Modulation for Parameter-Efficient Fine-Tuning
Jie Ou, Shuaihong Jiang, Yingjun Du +1
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, DoRA, and HiRA, enable lightweight adaptation of large pre-trained models via low-rank updates. However, existing PEFT…
cs.LG2025
Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE
Zhaokun Wang, Jinyu Guo, Jingwen Pu +5
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-han…