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

The trustworthy machine learning (ML) community is increasingly recognizing the crucial
need for models capable of selectively 'unlearning'data points after training. This leads to the …

Abstract Machine unlearning (MU) seeks to remove knowledge of specific data samples
from trained models without the necessity for complete retraining, a task made challenging …

Evaluating the bias in Large Language Models (LLMs) becomes increasingly crucial with
their rapid development. However, existing evaluation methods rely on fixed-form outputs …

Generating fair and accurate predictions plays a pivotal role in deploying large language
models (LLMs) in the real world. However, existing debiasing methods inevitably generate …

Abstract Machine Unlearning removes specific knowledge about training data samples from
an already trained model. It has significant practical benefits, such as purging private …

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 …

Direct Preference Optimization (DPO) is effective for aligning large language models (LLMs),
but when applied to multimodal models (MLLMs), it often favors text over image information …

This paper provides an overview of methods for measuring the stereotype bias of pre-trained
language models. It explains the term 'stereotype bias' and its measurement. A …

Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively
remove certain training samples, such as those with outdated or sensitive information, from …