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by zwaps
993 days ago
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Having literally done it in an enterprise setting (and participated in experiments for some of the largest companies in the world in their respective domain fields), I have to say: your lack of nuance and abundance of arrogance does not come across very well. It is important to distinguish between something being impossible, infeasible and not well understood. Fine-tuning "for effect" is mostly the latter. You say "current fine-tuning techniques can only contribute to knowledge indirectly" and then in the next post row back to "except in toy examples" because the former is - literally - not correct. This is HN. We are not advising clients on how "to get their data into their AI best". We can discuss here the actual technical detail of a thing. An intellectually honest discussion begins with saying: "From a scientific standpoint, and even from a practical standpoint, we are not sure yet, however..." |
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But you're correct, this is HN: so much pontificating without producing a single counterfactual implies you should speak for yourself and not the collective.
They said "LLM", but given the context it's an RLHF LLM, and presumably they want a generalized way to add factual information in a way that doesn't cripple the model's general performance (yes, I am being so arrogant as to draw obvious conclusions to give them a useful answer)
No paper on the subject has achieved this, the ones that come close (and by close I mean very far) fall back to BERT sized models which I already addressed below: so please petition your "enterprise" to share their secrets
(wrong crowd to get any gravitas out of the word enterprise btw, we understand it means "constrained usecase with minimal external validation")