On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Transformer-based large language models (LLMs) demonstrate impressive performance
across various natural language processing tasks. Serving LLM inference for generating …

Interacting with humans through multi-turn conversations is a fundamental feature of large
language models (LLMs). However, existing LLM serving engines executing multi-turn …

Large language models (LLMs) are increasingly used for complex tasks that require multiple
generation calls, advanced prompting techniques, control flow, and structured …

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 computational challenges of Large Language Model (LLM) inference remain a
significant barrier to their widespread deployment, especially as prompt lengths continue to …

Transformers are considered conceptually different from the previous generation of state-of-
the-art NLP models-recurrent neural networks (RNNs). In this work, we demonstrate that …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

Large language models (LLMs) are increasingly used for complex tasks that require multiple
generation calls, advanced prompting techniques, control flow, and structured …