Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

Pretrained language models sometimes possess knowledge that we do not wish them to,
including memorized personal information and knowledge that could be used to harm …

While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

Understanding model's sensitivity to its training data is crucial but can also be challenging
and costly, especially during training. To simplify such issues, we present the Memory …

The training of large language models (LLMs) necessitates substantial data and
computational resources, and updating outdated LLMs entails significant efforts and …

The increased attention to regulating the outputs of deep generative models, driven by
growing concerns about privacy and regulatory compliance, has highlighted the need for …

The performance of deep learning models heavily relies on the quality of the training data.
Inadequacies in the training data, such as corrupt input or noisy labels, can lead to the …