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

Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

Recent efforts have augmented large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

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

The complexity and emergent autonomy of Generative AI systems introduce challenges in
predictability and legal compliance. This paper analyses some of the legal and regulatory …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

Machine unlearning, the ability for a machine learning model to forget, is becoming
increasingly important to comply with data privacy regulations, as well as to remove harmful …

With privacy legislation empowering the users with the right to be forgotten, it has become
essential to make a model amenable for forgetting some of its training data. However …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …