Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …

Large language models (LLMs) have revolutionized natural language processing with their
exceptional capabilities. However, deploying LLMs on resource-constrained edge devices …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent text in response to prompts or …

Scaling model size significantly challenges the deployment and inference of Large
Language Models (LLMs). Due to the redundancy in LLM weights, recent research has …

To enhance the efficiency of language models, it would involve optimizing their training and
inference processes to reduce computational demands while maintaining high performance …

Large Language Models (LLMs) have revolutionized natural language processing tasks.
However, their practical application is constrained by substantial memory and computational …

The Large Language Models (LLMs) have been popular and widely used in creative ways
because of their powerful capabilities. However, the substantial model size and complexity …

With the rapid growth in the use of fine-tuning for large language models (LLMs), optimizing
fine-tuning while keeping inference efficient has become highly important. However, this is a …

Background: Given the fast-paced nature of today's technology, which has surpassed
human performance in tasks like image classification, visual reasoning, and English …