Large language models (LLMs) have achieved significant progress from pre-training on and
memorizing a wide range of textual data, however, this process might suffer from privacy …

Machine unlearning, the study of efficiently removing the impact of specific training instances
on a model, has garnered increased attention in recent years due to regulatory guidelines …

Large language models (LLMs) have advanced to encompass extensive knowledge across
diverse domains. Yet controlling what a large language model should not know is important …

Machine Learning models increasingly face data integrity challenges due to the use of large-
scale training datasets drawn from the Internet. We study what model developers can do if …

For privacy and security concerns the need to erase unwanted information from pre-trained
vision models is becoming evident nowadays. In real-world scenarios erasure requests …

Large language models trained on massive corpora of data from the web can memorize and
reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or …

Large language models (LLMs) have become the state of the art in natural language
processing. The massive adoption of generative LLMs and the capabilities they have shown …