Recent developments in deep learning have led to great success in various natural
language processing (NLP) tasks. However, these applications may involve data that …

Large Language Models (LLMs) have shown exceptional abilities for multiple different
natural language processing tasks. While prompting is a crucial tool for LLM inference, we …

Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in
Large Language Models (LLMs). While best-performing solutions in this domain use a plan …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant
attention in the era of large language models. However, existing models for MQA under KE …

With the rise of large language models (LLMs), ensuring they embody the principles of being
helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While …

Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent
privacy leakage, researchers have investigated word-level perturbations, relying on the …

Generalized eigenvalue problems (GEPs) find applications in various fields of science and
engineering. For example, principal component analysis, Fisher's discriminant analysis, and …

Privacy preservation has become a critical concern in high-dimensional data analysis due to
the growing prevalence of data-driven applications. Proposed by Li (1991), sliced inverse …