# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 3 Large Language Models for Networking
### Network Design and Planning  
The stage of network design and planning involves developing efficient and robust network architectures tailored to specific performance, security, and scalability needs. This process includes tasks such as topology design, resource allocation, and capacity planning. Incorporating LLMs into network design and planning can streamline these tasks by utilizing their advanced language comprehension and inference capabilities to generate innovative and optimized solutions. For example, NetLLM in [129] leverages pre-trained knowledge and powerful inference abilities to solve various networking tasks, achieving better performance and generalization than traditional methods. In [130], the authors propose using multi-agent generative LLMs in wireless networks to develop intelligent, self-governed networks that facilitate collaborative decision-making at the edge. Similarly, an LLM-based resource scheduling system using three LLM agents is proposed in [131] to translate user voice requests into resource allocation vectors for personalized optimization in wireless and digitalized energy networks, aiming to enhance user-centric approaches through natural language interfaces. llmqos [132] utilizes LLMs to extract information from natural language descriptions of web users and services, which is then combined with historical QoS data to enhance web service recommendations and QoS predictions. A cloud load balancing framework in [133] integrates reinforcement learning, LLMs, and edge intelligence to enhance performance, security, scalability, and operational efficiency for dynamic cloud environments. NetGPT [134], a collaborative cloud-edge framework, enhances personalized generative services and intelligent network management by combining LLMs with location-based information at the edge. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Process** & **Work** & **Year** & **Baseline Model(s)** & **Network Application(s)** \\ \hline \multirow{8}{*}{Network Design and Planning} & [PERSON] et al. [133] & 2023 & GPT-4 & Load Balancing, Capacity Planning \\  & [PERSON] et al. [130] & 2023 & GPT-4 & Intent-Based Networking, \\  & & & & Telecommunications, 6G, Network \\  & & & & Energy Saving \\  & NetLLM [129] & 2024 & LLaMA 2 & QoE, Bandwidth Management, Job \\  & & & & Scheduling, Adaptive \\  & & & & Bitrate Streaming \\  & & & & \\  & NetGPT [134] & 2024 & GPT-2, LLaMA & QoS, Network Provisioning, \\  & & & & Cloud-Edge Resource Allocation \\  & & & & and Scheduling \\  & & & & QoS, Network Service \\  & & & & Recommendation \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{8}{*}{Network Implementation} & \multirow{8}{*}{2023} & \multirow{8}{*}{GPT-4} & \multirow{8}{*}{Router Configuration} \\  & & & & & Configuration Validation \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & DeepSeek \\ \cline{1-1}  & NETBUDDY [137] & 2023 & GPT-4 & Network Configuration \\ \cline{1-1}  & Emergence [138] & 2023 & GPT-4, GPT-3.5 & Intent-Based Networking, Virtual \\ \cline{1-1}  & & & & Network Function, Network Policy \\ \cline{1-1}  & & & & \\ \cline{1-1}  & GPT-FL [139] & 2023 & GPT-3 & Federated Learning \\ \cline{1-1}  & & & & \\ \cline{1-1}  & LP-FL [140] & 2023 & BERT & Federated Learning \\ \cline{1-1}  & & & & \\ \cline{1-1}  & ChatAFL [141] & 2024 & GPT-3.5 & Cybersecurity, Network Protocol \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of selected works on LLMs for networking.