# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 2 Background
### From Natural Language to Network Configuration Language
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Natural Language Prompt** & **Network Operation** & **Features** & **Example** \\ \hline Distribute incoming traffic & Load Balancing & Distribute traffic using a & ip cef load-sharing algorithm \\ evenly across three servers to & Configuration & 4-tuple (source Internet & include-ports source destination \\ ensure optimal performance. & & Protocol (IP), destination IP, source port, destination port) & gtp \\  & & and utilize unique identifiers & to avoid traffic polarization, supporting both IPv4 and IPv6 headers. & \\ \hline Configure the router to & QoS Configuration & Prioritize specific types of & interface GigabitEthernet0/1 \\ prioritize voice traffic over & & traffic by applying pre-defined & service-policy output \\ other types of traffic. & & policies to network interfaces. & PRIORITIZE-VOICE \\ \hline Analyze the firewall logs to & Log Analysis & Filter and search through log & grep “Failed login” \\ identify any failed login & & entries based on criteria. & /var/log/firewall.log \\ attempts. & & & \\ Block all external traffic from & Firewall Rule & Establish access control by & deny ip any host 198.168.100.2 \\ accessing the internal server & Configuration & & defining rules to block \\ located at 198.168.100.2 to & & unauthorized external traffic \\ enhance security. & & to specific internal server & IP addresses. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The examples of translating natural language commands into specific network operations using GPT-4. The disparity between the conversational nature of natural language and the precise, rule-bound nature of network configuration language necessitates advanced solutions to seamlessly bridge these two forms of communication, translating user intents into accurate network commands. LLMs have the potential to significantly enhance network quality of experience (QoE) by offering customized responses to specific natural language inputs from users. These models can interpret the nuanced requirements conveyed through natural language and convert them into precise network operations. Techniques such as fine-tuning, prompt engineering, and RAG play crucial roles in this process. For example, LLMs can collaborate with IBN by fine-tuning to align with specific network policies and protocols, enabling the accurate translation of high-level user intents into precise network configurations. In SDN, prompt engineering can be used to create specific input prompts that help LLMs generate dynamic control commands, adjusting network resources in real time based on user requirements. For GO communication, RAG can integrate external knowledge bases, allowing LLMs to access the latest domain-specific information and optimize network performance to meet specific objectives, such as minimizing latency or maximizing throughput. These advanced techniques, which translate natural language into network configuration language, collectively enhance the precision, responsiveness, and adaptability of network management systems.