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
- Notes from Bing Chat—Our First Encounter With Manipulative AI - 19th November 2024
- Project: Civic Band - scraping and searching PDF meeting minutes from hundreds of municipalities - 16th November 2024
- Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac - 12th November 2024