A big AI model trained on huge amounts of text that writes by picking the next token.
A large language model (LLM) is a big AI model trained on an enormous amount of text: web pages, books, code. What it does underneath is simple. It looks at the text so far, picks the next token, adds it, and repeats.
"Large" means billions to hundreds of billions of parameters, or more. Most are built on the Transformer design from 2017. In 2020 GPT-3 surprised people by picking up new tasks from a few examples in the prompt, and in 2022 ChatGPT put an LLM behind a chat window anyone could use.
Because it picks what sounds plausible next, it can confidently state false things. This is called hallucination, so check anything important.
Think of it like this
Think of the next-word suggestions on your phone keyboard: type "what's for" and it offers "lunch". An LLM is that feature grown on an unimaginable amount of text, until it can keep going for a paragraph or a whole essay.
For example
Replies seem to stream out bit by bit because the model really is picking and adding one token at a time.
Records about this
Source-checked moments from AI history.
The conversational UI opened prompting to non-experts, turning a skill of a few practitioners into a mass phenomenon
Introduced in-context learning, which performs a task from examples in the prompt alone, without updating parameters
Proposed a neural language model that learns words as dense vectors. The direct predecessor of the embedding lineage that includes word2vec