Large Language Models
Large Language Models (LLMs) are a type of Artificial Intelligence that use Deep Learning techniques and massive datasets to understand, generate, and process human language in a highly sophisticated manner.
During the Modeling Process, LLMs are trained on vast amounts of text data, such as books, articles, and websites, allowing them to learn the Attention patterns, structures, and relationships between words, phrases, and sentences. This enables LLMs to develop a deep understanding of language, including grammar, facts, reasoning abilities, and even some biases present in the data. Once trained, LLMs can be used for a wide range of natural language processing tasks, such as text generation, question answering, translation, and summarization.
The largest and most capable LLMs, such as Generative Pre-trained Transformers like OpenAI GPT based models, are built using Transformer Neural Network architectures, which allow them to capture long-range Attention dependencies between words and understand context.
Despite their impressive capabilities, LLMs also face challenges, such as ensuring the accuracy and reliability of the generated content, mitigating biases, and addressing potential ethical issues related to the misuse of language.
LLMs are one subset of Foundation Models as illustrated below:
