Earlier quoted context omitted.
Sorry -- I keep seeing this being used but I'm not entirely sure how it differs from most of human thinking. Most human 'reasoning' is probabilistic as well and we rely on 'associative' networks to ingest information. In a similar manner - LLMs use association as well -- and not only that, but they are capable of figuring out patterns based on examples (just like humans are) -- read this paper for context: https://ar…
You seem possibly more knowledgeable then me on the matter. My impression is that LLMs predict the next token based on the prior context. They do that by having learned a probability distribution from tokens -> next-token. Then as I understand, the models are never reasoning about the problem, but always about what the next token should be given the context. The chain of thought is just rewarding them so that the nex…
How you find the function that does the mapping probably doesn't matter. We use probability theory and information theory, because they're the best tools for the job, but there's nothing to say you couldn't handcraft it from scratch if you were some transcendent creature.