>Certainly not? Composite systems that leverage LLMs can do a lot of things - but AFAIU LLMs will likely never rationalize or be able to "add numbers" in the normal sense; they can count only in as much they know that 1,2,3,4 is more probably coming after "count to four" than 10,2,4.
LLMs can add numbers just fine. arithmetic is one of the easiest domains to test on data it'd never have seen in training.
I can see what you are getting at, but the underlying implementation of LLMs really is reliant on non-deterministic, random sampling of the underlying model. The random sampling is weighted to favor certain selections over others, but rare selections are possible.
Its intrinsically built on a degree of randomness. Though it is also unfair to say something like "produce random output" as the probabilities themselves make it less than fully random.
Not really. Nothing random about the output of LLMs. https://www.nature.com/articles/s41587-022-01618-2
>Certainly not? Composite systems that leverage LLMs can do a lot of things - but AFAIU LLMs will likely never rationalize or be able to "add numbers" in the normal sense; they can count only in as much they know that 1,2,3,4 is more probably coming after "count to four" than 10,2,4.
LLMs can add numbers just fine. arithmetic is one of the easiest domains to test on data it'd never have seen in training.