We explore machine unlearning in the domain of large language models (LLMs), referred to
as LLM unlearning. This initiative aims to eliminate undesirable data influence (for example …

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 …

Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge
from their training corpora, the memorization of sensitive information in the corpora such as …

The problem of pre-training data detection for large language models (LLMs) has received
growing attention due to its implications in critical issues like copyright violation and test data …

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

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …

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 …

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 …

On-device training is currently the most common approach for training machine learning
(ML) models on private, distributed user data. Despite this, on-device training has several …

Pretrained large language models (LLMs) have revolutionized natural language processing
(NLP) tasks such as summarization, question answering, and translation. However, LLMs …