Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning

We improve unlearning efficacy by introducing random labeling loss and ensuring the model retains its general-purpose knowledge by adjusting targeted parameters with gradient-based weight saliency.