What is a Large Language Model?

A Large Language Model (LLM) is an artificial intelligence model trained by deep learning algorithms to recognize, generate, translate, and/or summarize large amounts of written human language and text data. Large Language Models are one of the most advanced and easy-to-use natural language processing (NLP) solutions available today.

Large Language Models have a wide range of applications, including language translation, chatbot and content creation, text summarization, and can also be used to improve search engines, voice assistants, and virtual assistants.

How do large language models work?

Large language models work primarily through their specialized converter architecture and large training data sets.

For a large language model to work, it must first be trained on a large amount of textual data to make context, relationships, and text patterns clear. This data can come from many sources, such as websites, books, and historical records. wikipedia and GitHub are two of the larger web-based samples used for LLM training. Regardless of the source, the training data must be cleaned and quality-checked before it can be used to train the LLM.

Once the data is cleaned and ready for training, it can be tokenized, or broken down into smaller parts for easier understanding. Tokens can be words, special characters, prefixes, suffixes, and other linguistic components that make contextual meaning clearer. Tokens also inform the attention mechanism of the large language model, or its ability to quickly and intelligently focus on the most relevant parts of the input text so that it can predict and/or generate appropriate output.

Once a large language model has received initial training, it can be deployed to users through various forms, including chatbots. However, enterprise users access large language models primarily through APIs that allow developers to integrate LLM functionality into existing applications.

Large Language Models are trained primarily through unsupervised, semi-supervised, or self-supervised learning, and LLMs can adjust their internal parameters and effectively "learn" from new user input over time.