It's a bit sad and confusing that LLMs ("Large Language Models") have little to do with language; It's just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something.
They don't care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it".
Recent articles
- LLM 0.22, the annotated release notes - 17th February 2025
- Run LLMs on macOS using llm-mlx and Apple's MLX framework - 15th February 2025
- URL-addressable Pyodide Python environments - 13th February 2025