Although large language models (LLMs) are widely deployed, the data used to train them is
rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but …

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 …

The race to train language models on vast, diverse, and inconsistently documented datasets
has raised pressing concerns about the legal and ethical risks for practitioners. To remedy …

Foundation model development attracts a rapidly expanding body of contributors, scientists,
and applications. To help shape responsible development practices, we introduce the …

General-purpose artificial intelligence (AI) systems are built on massive swathes of public
web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge …

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

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 …

Language models (LMs) derive their capabilities from extensive training on diverse data,
including potentially copyrighted material. These models can memorize and generate …

Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a
member of a target model's training data. Despite extensive research on traditional machine …

Large Language Models (LLMs) are advancing at a remarkable pace, with myriad
applications under development. Unlike most earlier machine learning models, they are no …