> I suspect ... still wins in general world knowledge due to bigger size
replyEncyclopedic knowledge matters relatively little in perspective, given the expectable future developments: even the more knowledgeable of us will use that knowledge for reasoning and intuition (and we will have absorbed the intellectual keys during our training), but under our professional hat we should in theory be ready to go "I stand corrected" and "more precisely" with the actual data at hand.
I.e.: for the encyclopedic knowledge needed, the /understander/ will have a RAG subsystem and a corpus of knowledge to inquire upon processing queries.
(Corroboration: we can't delirate, and neither can the machine...)
Don't LLMs work on attention though? The closer in their hyperdimensional space you can land your problem to their inherent understand the better they are at understanding your problem domain. RAG loops can be very slow and agents may simply lack the knowledge to use them correctly.
replyI agree with you in general, but depending on the task I also find that a certain level of encyclopedic knowledge can be very valuable. For example, if you use it for coding, the model will likely not resort to search or RAGs when deciding whether to use a particular package or stack.
replyA great position to take. Strong opinions, weakly held.
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