# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 2 Background
### Large Language Models  
A large language model (LLM) is an advanced computational model renowned for its ability to perform a wide range of NLP tasks such as general-purpose language generation, text classification, and sentiment analysis. LLMs are built upon sophisticated language models and acquire their capabilities by learning intricate statistical relationships from extensive corpora of text. The training process for LLMs is both computationally intensive and data-rich, involving self-supervised and semi-supervised learning techniques. During this process, the models are exposed to vast datasets, enabling them to learn diverse linguistic patterns and contextual nuances. LLMs are particularly effective in text generation, a prominent application of GenAI. They operate by taking an initial input text and recursively predicting the next word or token, thereby constructing coherent and contextually appropriate sentences and paragraphs. This predictive mechanism enables LLMs to generate human-like text that is not only grammatically correct but also contextually relevant and nuanced. The versatility of LLMs extends to various NLP applications such as machine translation, which is converting text from one language to another, and text summarization, which is condensing long documents into concise summaries. The impact of LLMs has been profound, especially after being incorporated as a chat tool that can understand and respond to inquiries with high accuracy. This integration allows for real-time interactions, promptly addressing problems and significantly reducing the time and effort required by humans. LLMs are advanced artificial neural networks that employ the transformer architecture, a revolutionary development in deep learning. Originally introduced in [78], the trans  
Figure 1: The comprehensive life cycle of network systems, highlighting the four primary stages in network engineering: Network Design and Planning, Network Implementation, Network Analytics, and Network Management. Each stage involves critical tasks such as resource scheduling, deployment, traffic analysis, and security protection. These four network engineering stages are interconnected, ensuring seamless management of the network from initial planning to ongoing optimization and protection. Network intelligence is pivotal in automating processes and improving the efficiency, accuracy, and reliability of network tasks across all stages. former model uses an encoder-decoder structure designed to handle sequence-to-sequence tasks efficiently. The transformer model architecture is shown in Figure 2. The encoder processes the input sequence, capturing relevant information, while the decoder generates the output sequence by attending to the output of the encoder and previously generated tokens. This dual structure enables the transformer to excel in NLP tasks, where understanding and generating sequences of text are critical. The mathematical foundation of this architecture involves self-attention mechanisms, which allow the model to dynamically weigh the importance of different words in a sentence. The encoder-decoder structure works by first passing the input sequence through the encoder layers, which consist of multi-head self-attention and feed-forward neural networks. The self-attention mechanism can be mathematically represented as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V,\]  
where \(Q\), \(K\), and \(V\) are the query, key, and value matrices derived from the input embeddings, and \(d_{k}\) is the dimension of the keys. This mechanism allows the model to focus on different parts of the input sequence, capturing long-range dependencies and contextual relationships. In the decoder, a similar self-attention mechanism is used, but with an additional encoder-decoder attention layer that attends to the output of the encoder. This allows the decoder to generate the output sequence while considering the entire input sequence, enhancing the coherence and relevance of the generated text.