# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 1 Introduction  
The evolution of network technologies has dramatically transformed the way we communicate, share information, and connect with the world [1; 2; 3]. Traditional networks, which rely heavily on static configurations and manual interventions, are foundational to modern infrastructure but come with significant challenges. These networks require complex management and operational expertise, making them difficult for non-experts to handle. Meanwhile, as networks increasingly grow in size and complexity, managing them becomes even more daunting. Issues such as network configuration, optimization, troubleshooting, and security in large-scale network environments managed through labor-intensive processes can lead to inefficiencies and human errors [4]. As the demand for more agile, resilient, and secure networks grows, these traditional methods fall short, highlighting the need for more intelligent and automated solutions. Over the past decade, artificial intelligence (AI) has profoundly reshaped the landscape of computer technologies, driving advancements across numerous sectors [5; 6]. Building on this momentum, AI has emerged as a transformative force in addressing the limitations of traditional network management. AI algorithms can deliver high-quality solutionssuch as automating network configuration, optimizing traffic flows, enhancing security protocols, and predicting network failures before they occur, thereby significantly reducing the need for manual interventions and minimizing human errors [3; 7]. For instance, machine learning techniques analyze vast amounts of network data to identify patterns and anomalies, enabling predictive maintenance that can foresee hardware failures and trigger preventive actions, thus minimizing downtime and maintaining network reliability [8; 9]. The application of AI in networking also enhances network security by automatically detecting and responding to threats in real time, which is especially critical in large-scale, dynamic environments [1]. To enhance AI algorithms, mathematical optimization theory is a common approach [10; 11; 12; 13]. However, AI-driven solutions are often inherently complex and unexplainable for human understanding. This lack of interpretability significantly limits the commercial adoption of AI-based solutions in practice [14]. To address these challenges, generative AI (GenAI), the next frontier in AI, can play a pivotal role in transforming networks. GenAI encompasses generative models capable of creating content and understanding complex patterns across various domains. These models can generate text, images, videos, music, and code, demonstrating a sophisticated grasp of the data they process. Consequently, the applications of GenAI are wide-ranging, from aiding creative industries and automating routine tasks to advancing scientific research through simulation and hypothesis generation. Recently, large language models (LLMs) have drawn considerable attention from the research community as a groundbreaking technology in the field of GenAI. LLMs excel in natural language processing (NLP) tasks, such as language translation, text summarization, and sentiment analysis. These models can interpret and generate human-like text based on extensive datasets, making them powerful tools for any field that involves intricate linguistic tasks. To further enhance the capabilities of LLMs, techniques such as prompt engineering [15; 16; 17; 18], fine-tuning [19; 20], and retrieval-augmented generation (RAG) [21; 22] can be employed to develop domain-specific LLMs [23; 24; 25; 26; 27]. These methods enable LLMs to effectively solve specific tasks in various domains, including network engineering. LLMs are currently a highly popular research area, with the technology still in its early stages of development, offering numerous potential avenues for exploration and innovation. Meanwhile, network engineering remains a critical and long-established field facing numerous complex challenges.