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 …

Abstract Machine unlearning has become a pivotal task to erase the influence of data from a
trained model. It adheres to recent data regulation standards and enhances the privacy and …

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …

With increasing privacy concerns in artificial intelligence, regulations have mandated the
right to be forgotten, granting individuals the right to withdraw their data from models …

The need for effective unlearning mechanisms in large language models (LLMs) is
increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical …

Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but
they also pose safety risks, such as the potential generation of harmful content and copyright …

Large language models (LLMs) have shown remarkable proficiency in generating text,
benefiting from extensive training on vast textual corpora. However, LLMs may also acquire …

In this work, we address the problem of large language model (LLM) unlearning, aiming to
remove unwanted data influences and associated model capabilities (eg, copyrighted data …

Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while
retaining the overall utility of the model. As a multi-task learning problem, MU involves …

We present the findings of the first NeurIPS competition on unlearning, which sought to
stimulate the development of novel algorithms and initiate discussions on formal and robust …