In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …

Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data
during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from …

Large language models (LLMs) can often be made to behave in undesirable ways that they
are explicitly fine-tuned not to. For example, the LLM red-teaming literature has produced a …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

Generative AI technologies have been deployed in many places, such as (multimodal) large
language models and vision generative models. Their remarkable performance should be …

Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract
their data from machine learning models on request. Shortly after, inexact schemes were …

LLMs are known to be vulnerable to jailbreak attacks, even after safety alignment. An
important observation is that, while different types of jailbreak attacks can generate …