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 revolutionized natural language processing tasks.
However, their practical deployment is hindered by their immense memory and computation …

Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

The inference process in Large Language Models (LLMs) is often limited due to the absence
of parallelism in the auto-regressive decoding process, resulting in most operations being …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

The rapid growth of Large Language Models (LLMs) has been a driving force in
transforming various domains, reshaping the artificial general intelligence landscape …

The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated
models like OpenAI's ChatGPT, represents a significant advancement in artificial …

Large language models (LLMs) are crucial in modern natural language processing and
artificial intelligence. However, they face challenges in managing their significant memory …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

The emergence of accurate open large language models (LLMs) has led to a race towards
quantization techniques for such models enabling execution on end-user devices. In this …