A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massive amounts of data to understand, summarize, generate, and predict natural language text. LLMs are characterized by their large size, which is enabled by AI accelerators that can process vast amounts of text data, mostly scraped from the Internet. These models are trained on unlabeled text data from various sources, such as Common Crawl, The Pile, MassiveText, Wikipedia, and GitHub.
LLMs are general-purpose models that excel at a wide range of tasks, as opposed to being trained for one specific task like sentiment analysis or named entity recognition. They can be used for various natural language processing (NLP) tasks, including recognizing, summarizing, translating, predicting, and generating content. Some well-known examples of LLMs include GPT-3 and GPT-4 from OpenAI, LLaMA from Meta, and PaLM2 from Google.
The training process for LLMs involves feeding them large amounts of data, such as books, articles, or web pages, so they can learn the patterns and connections between words. The more data an LLM is trained on, the better it will be at generating new content. Once a large language model has been trained, it can be used to generate new content based on the parameters set by the user. For example, a user could provide a prompt, such as a sentence or paragraph, and the LLM would generate the rest of the article based on the patterns and connections it has learned from analyzing similar text.
