Earlier quoted context omitted.
Unfortunately, your correction is still misleading because it fails to capture what really sets GPT-3 apart. Ironically, GPT-3's limitation also highlights its strength. As it is not capable of learning (few shot or otherwise) in the strict sense of permanently changing its parameters based on examples, all its demonstrated capabilities are completely at inference time. It somehow configures itself at inference time…
> It somehow configures itself at inference time so that state machines which produce plausible continuations of whatever pattern it was fed, are most probably generated. What is this sentence supposed to convey? I'm an NLP practicioner/researcher and this isn't even true - as GPT isn't a "state machine" as the latent space is continuous and not finite. Moreover, there is nothing that makes GPT-3 "not capable of lear…
There are examples where it is able to recognize and continue patterns in strings which if manually generated, would have required a FSM. In fact, some of the more impressive examples would require a stack of some sort so I thought I was rather underselling its capabilities in that arena.
> as GPT isn't a "state machine" as the latent space is continuous and not finite.
Technically speaking, that is impossible since these models leverage floating point numbers and are limited in memory to whatever hidden and self-attention layers.
Practically speaking, in order to generate strings based on patterns as mentioned prior, there must be abstract states which correspond to states and state changes such that thinking in terms of at least state machines is useful.
Studying LMs in terms of automata is not strange, there have been papers which do this for specific trained RNNs (such as https://arxiv.org/abs/1711.09576). I contend GPT-3 is capable, to a certain extent, of generating these dynamically at inference time.
As far as I know this way of extracting what LMs are doing hasn't been done for Transformers but you can also frame Transformers in term of RNNs so there's no reason why such methods wouldn't readily apply to them too.
> Moreover, there is nothing that makes GPT-3 "not capable of learning."
I specifically addressed that: to count as learning, without diluting the utility of the term, it has to be capable of remembering. Without permanent changes to its weights, the use of the term learning stretches the word beyond utility.