Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with
pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning …

Transformers have revolutionized natural language processing (NLP) and have had a huge
impact on automating tasks. Recently, transformers have led to the development of powerful …

Attention is a fundamental component behind the remarkable achievements of large
language models (LLMs). However, our current understanding of the attention mechanism …

The deployment of Large Language Models (LLMs) in diverse applications necessitates an
assurance of safety without compromising the contextual integrity of the generated content …

In recent advancements, large language models (LLMs) have demonstrated remarkable
capabilities in diverse tasks, primarily through interactive question-answering with humans …

This survey paper investigates how personalized learning offered by Large Language
Models (LLMs) could transform educational experiences. We explore Knowledge Editing …

While various vertical domain large language models (LLMs) have been developed, the
challenge of automatically evaluating their performance across different domains remains …

Toxicity and bias in online conversations hinder respectful interactions, leading to issues
such as harassment and discrimination. While advancements in natural language …

Large language models (LLMs) have demonstrated impressive success in a wide range of
natural language processing (NLP) tasks due to their extensive general knowledge of the …

Low-Rank Adaptation (LoRA) is a computationally efficient approach for fine-tuning large
pre-trained language models, designed to reduce memory and computational overhead by …