Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Large Language Models (LLMs) have revolutionized natural language processing tasks with
remarkable success. However, their formidable size and computational demands present …

Large language models (LLMs) have revolutionized natural language processing tasks.
However, their practical deployment is hindered by their immense memory and computation …

Quantization is an indispensable technique for serving Large Language Models (LLMs) and
has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where …

Recently years have witnessed a rapid development of large language models (LLMs).
Despite the strong ability in many language-understanding tasks, the heavy computational …

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

As the size of large language models continue to scale, so does the computational
resources required to run it. Spiking Neural Networks (SNNs) have emerged as an energy …

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 …

Large Language Models (LLMs) with billions of parameters have drastically transformed AI
applications. However, their demanding computation during inference has raised significant …