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cs.CL2025
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning
Baohao Liao, Christian Herold, Seyyed Hadi Hashemi +3
As large language models (LLMs) scale, model compression is crucial for edge deployment and accessibility. Weight-only quantization reduces model size but suffers from performance…
cs.CL2025
Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation
Stefan Vasilev, Christian Herold, Baohao Liao +3
This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specifi…