The White House Executive Order on Artificial Intelligence highlights the risks of large
language models (LLMs) empowering malicious actors in developing biological, cyber, and …

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 …

With advancements in self-supervised learning, the availability of trillions tokens in a pre-
training corpus, instruction fine-tuning, and the development of large Transformers with …

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 …

Large Language Models (LLMs) frequently memorize long sequences verbatim, often with
serious legal and privacy implications. Much prior work has studied such verbatim …

Generative AI technologies have been deployed in many places, such as (multimodal) large
language models and vision generative models. Their remarkable performance should be …

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

Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data
during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from …

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast
potential across various domains, attributed to their extensive pretraining knowledge and …

Conclusion In this letter, we propose E2URec, the efficient and effective unlearning method
for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only …