A large language model (LLM) is a type of artificial intelligence (AI) program designed to understand, process, and generate human language. LLMs are built using deep learning techniques, specifically on neural network architectures called transformers, which enable them to handle sequences of text by understanding the context and relationships between words
. These models are trained on massive datasets containing vast amounts of text-ranging from books and articles to web pages and conversations-often measured in terabytes or even petabytes of data. This extensive training allows LLMs to learn grammar, semantics, and language patterns in a self- supervised way, meaning they learn from the data without explicit labeling
. LLMs have billions to hundreds of billions of parameters-values the model adjusts during training to improve its predictions. For example, GPT-3, a well-known LLM, has 175 billion parameters and was trained on 45 terabytes of text data
. The transformer architecture allows these models to process entire sequences in parallel, significantly speeding up training compared to previous models. LLMs can perform a wide range of natural language processing tasks such as text generation, summarization, translation, question answering, and even code generation. They are widely used in applications like chatbots, virtual assistants, content creation, and software development assistance
. In summary, an LLM is a large-scale AI model specialized in understanding and generating human language by leveraging transformer neural networks trained on enormous text datasets, enabling it to perform diverse language- related tasks with high proficiency
