Large language models are finetuned to refuse questions about hazardous knowledge, but
these protections can often be bypassed. Unlearning methods aim at completely removing …

Machine unlearning is a promising approach to mitigate undesirable memorization of
training data in ML models. However, in this work we show that existing approaches for …

Unlearning methods have the potential to improve the privacy and safety of large language
models (LLMs) by removing sensitive or harmful information post hoc. The LLM unlearning …

LLM have achieved success in many fields but still troubled by problematic content in the
training corpora. LLM unlearning aims at reducing their influence and avoid undesirable …

With increasing privacy concerns in artificial intelligence, regulations have mandated the
right to be forgotten, granting individuals the right to withdraw their data from models …

Large language models (LLMs) may memorize sensitive or copyrighted content, raising
privacy and legal concerns. Due to the high cost of retraining from scratch, researchers …

Machine unlearning aims to efficiently eliminate the influence of specific training data,
known as the forget set, from the model. However, existing unlearning methods for Large …

Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive
data, such as personal privacy information and copyrighted material. Recent advancements …

The sheer scale of data required to train modern large language models (LLMs) poses
significant risks, as models are likely to gain knowledge of sensitive topics such as bio …

With the rapid advancement of Multimodal Large Language Models (MLLMs), a variety of
benchmarks have been introduced to evaluate their capabilities. While most evaluations …