Comment by bitexploder
5 months ago
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.
But, in short, the ability to manage information, to process it properly, is more important in this regard than just having the information. "Having" more knowledge is not a guarantee to "using" it better.
And to improve reliability, if the machine can check, it will have to check. "Costly" cannot be an excuse.
Understanding of a specific problem space can be a prerequisite to be able to form a proper query (i.e. to ask the correct question).
Model doesn't know what it doesn't know.
Your suggestion is not clear: yes we reason and define relevant details (maybe through further information retrieval) to better construct queries - that is what Analytical school of thought taught and insisted on -, and even more crucial is that the subsequent delegated steps, of constructing replies, imply reasoning and information retrieval.
Said abilities - intellectual strength - are immensely more important than notions. The relation between network size and intellectual strength, vs network size and notions (original topic in this branch), is presumably not yet that clear. Intelligent models may not necessarily be embedded with explicit information of everything, though they will have to have ways to reach that upon contingent necessity (to solve specific problems). Like us.
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