We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …

The recent advances in diffusion models (DMs) have revolutionized the generation of
realistic and complex images. However, these models also introduce potential safety …

While LLMs have demonstrated impressive performance across various domains and tasks,
their security issues have become increasingly severe. Machine unlearning (MU) has …

Machine unlearning is a promising approach to mitigate undesirable memorization of
training data in ML models. However, in this work we show that existing approaches for …

With the rapid progress of diffusion-based content generation, significant efforts are being
made to unlearn harmful or copyrighted concepts from pretrained diffusion models (DMs) to …

Representation Misdirection for Unlearning (RMU), which steers model representation in the
intermediate layer to a target random representation, is an effective method for large …

We articulate fundamental mismatches between technical methods for machine unlearning
in Generative AI, and documented aspirations for broader impact that these methods could …

Machine unlearning aims to efficiently eliminate the influence of specific training data,
known as the forget set, from the model. However, existing unlearning methods for Large …

Large language models (LLMs) may memorize sensitive or copyrighted content, raising
privacy and legal concerns. Due to the high cost of retraining from scratch, researchers …

As AI systems become more capable, widely deployed, and increasingly autonomous in
critical areas such as cybersecurity, biological research, and healthcare, ensuring their …