Large Language Models for Networking: Workflow, Advances and Challenges
arxiv(2024)
摘要
The networking field is characterized by its high complexity and rapid
iteration, requiring extensive expertise to accomplish network tasks, ranging
from network design, diagnosis, configuration and security. The inherent
complexity of these tasks, coupled with the ever-changing landscape of
networking technologies and protocols, poses significant hurdles for
traditional machine learning-based methods. These methods often struggle to
generalize and automate complex tasks in networking, as they require extensive
labeled data, domain-specific feature engineering, and frequent retraining to
adapt to new scenarios. However, the recent emergence of large language models
(LLMs) has sparked a new wave of possibilities in addressing these challenges.
LLMs have demonstrated remarkable capabilities in natural language
understanding, generation, and reasoning. These models, trained on extensive
data, can benefit the networking domain. Some efforts have already explored the
application of LLMs in the networking domain and revealed promising results. By
reviewing recent advances, we present an abstract workflow to describe the
fundamental process involved in applying LLM for Networking. We introduce the
highlights of existing works by category and explain in detail how they operate
at different stages of the workflow. Furthermore, we delve into the challenges
encountered, discuss potential solutions, and outline future research
prospects. We hope that this survey will provide insight for researchers and
practitioners, promoting the development of this interdisciplinary research
field.
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