I'll just add the observation that when we do this it's largely based on feedback receive from the recipient (well, so long as you're talking-with as opposed to talking-at) - we're paying attention to how the audience is paying attention or not, any small facial tics that might betray skepticism or agreement and so on. I'm looking forward to interacting with an LLM that pairs an emotion-vector along with each token it has previously produced.
hume.ai goes a long way analyzing audio, just a matter of time before they're ingesting realtime facial cues to also incorporate their audience's reaction in their choice of what to say next
I don't know how the reasoning part comes to us but if we could implant that capability to a transformer model then it would end up pretty good.
I think it's fair to ask whether these are essential techniques for improving precision and clarity, or just a way to compensate for not being able to see the whole picture all at once - but if the latter is the case, there's still room for improvement in LLMs (and me, for that matter.) I notice that experts on a topic are often able to pick out what matters most without any apparent hesitation.
Sure, it's a strength given that transformers are currently limited by compute budget, but theoretically, if we were to have a way to overcome this, it seems obvious to me that transformer's 'one-shot' ability makes them better.
That being said the recursive aspect you're referencing can be built into a transformer as well. This is a sampling and training problem.
The innovation is not the speed, but the lack of recursion or iteration. Humans, even accomplished ones, have to reread sections and really 'internalize' ideas before being able to summarize and very few humans can -- in a single attempt -- generate perfect speech. Most of us speak and unknowingly revise our own speech as we go along. Unlike transformers, that speak confidently, we start making a sentence and then decide halfway through its not going where we like. Then we start it over again, and by the powers of human attention, no one seems to really notice.
Transformers Are just insanely complicated and expensive to train.