Large Language Model Adaptation for Networking

Many networking tasks now employ deep learning (DL) to solve complex prediction and system optimization problems. However, current design philosophy of DL-based algorithms entails intensive engineering overhead due to the manual design of deep neural networks (DNNs) for different networking tasks. Besides, DNNs tend to achieve poor generalization performance on unseen data distributions/environments.
Motivated by the recent success of large language models (LLMs), for the first time, this work studies the LLM adaptation for networking to explore a more sustainable …

algorithms cs.lg cs.ni current data deep learning design engineering environments language language model large language large language model model adaptation networking networks neural networks optimization performance philosophy prediction solve tasks