Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target
  Self-Distillation
Authors: name: Stefan Vasilev, name: Christian Herold, name: Baohao Liao, name: Seyyed Hadi Hashemi, name: Shahram Khadivi, name: Christof Monz

This paper introduces Unilogit, a novel self-distillation method for machine
unlearning in Large Language Models. Unilogit addresses the challenge of
selectively forgetting specific information while maintaining overall model
utility, a critical task in compliance with data privacy regulations like GDPR.
Unlike prior methods that rely on static hyperparameters or starting model
outputs, Unilogit dynamically adjusts target logits to achieve a uniform
probability for the target token, leveraging the current model's outputs for
more accurate self-distillation targets. This approach not only eliminates the
need for additional hyperparameters but also enhances the model's ability to
approximate the golden targets. Extensive experiments on public benchmarks and
an in-house e-commerce dataset demonstrate Unilogit's superior performance in
balancing forget and retain objectives, outperforming state-of-the-art methods
such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness
across various scenarios, highlighting its practical applicability and
effectiveness in achieving efficacious machine unlearning.