The rapid development of large language models (LLMs) has opened new avenues across
various fields, including cybersecurity, which faces an evolving threat landscape and …

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 …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

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 …

Language models (LMs) are trained on vast amounts of text data, which may include private
and copyrighted content. Data owners may request the removal of their data from a trained …

Recent work has demonstrated that finetuning is a promising approach to'unlearn'concepts
from large language models. However, finetuning can be expensive, as it requires both …

Generative models are rapidly gaining popularity and being integrated into everyday
applications, raising concerns over their safe use as various vulnerabilities are exposed. In …

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 …

This is the interim publication of the first International Scientific Report on the Safety of
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …