But I feel we're going back full circle. These small models are not generalist, thus not really LLMs at least in terms of objective. Recently there has been a rise of "specialized" models that provide lots of values, but that's not why we were sold on LLMs.
But that's the thing, I don't need my ML model to be able to write me a sonnet about the history of beets, especially if I want to run it at home for specific tasks like as a programming assistant.
I'm fine with and prefer specialist models in most cases.
I would love a model that knows SQL really well so I don't need to remember all the small details of the language. Beyond that, I don't see why the transformer architecture can't be applied to any problem that needs to predict sequences.
Specialized models work much better still for most stuff. Really we need an LLM to understand the input and then hand it off to a specialized model that actually provides good results.
I think playing word games about what really counts as an LLM is a losing battle. It has become a marketing term, mostly. It’s better to have a functionalist point of view of “what can this thing do”.
