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 …

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

Generative artificial intelligence (AI) systems are trained on large data corpora to generate
new pieces of text, images, videos, and other media. There is growing concern that such …

Large language models (LLMs) achieve good performance on challenging reasoning
benchmarks, yet could also make basic reasoning mistakes. This contrasting behavior is …

Evaluating the degree of reproduction of copyright-protected content by language models
(LMs) is of significant interest to the AI and legal communities. Although both literal and non …

The rapid progress of generative AI technology has sparked significant copyright concerns,
leading to numerous lawsuits filed against AI developers. While various techniques for …

Machine unlearning algorithms, designed for selective removal of training data from models,
have emerged as a promising approach to growing privacy concerns. In this work, we …

Machine unlearning algorithms are increasingly important as legal concerns arise around
the provenance of training data, but verifying the success of unlearning is often difficult …

Structural information in images is crucial for aesthetic assessment, and it is widely
recognized in the artistic field that imitating the structure of other works significantly infringes …

Calls for transparency in AI systems are growing in number and urgency from diverse
stakeholders ranging from regulators to researchers to users (with a comparative absence of …