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AI trained on AI garbage spits out AI garbage. (www.technologyreview.com)

submitted 4 months ago by ModerateImprovement@sh.itjust.works to c/technology@lemmy.world

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[–] barsoap@lemm.ee 2 points 4 months ago* (last edited 4 months ago) (1 children)

because that phrase doesn’t ever appear in the training data.

Eh but LLMs abstract. It has seen " have feathers" and " have fur" quite a lot of times. The problem isn't that LLMs can't reason at all, the problem is that they do employ techniques used in proper reasoning, in particular tracking context throughout the text (cross-attention) but lack techniques necessary for the whole thing, instead relying on confabulation to sound convincing regardless of the BS they spout. Suffices to emulate an Etonian but that's not a high standard.

[–] FaceDeer@fedia.io 1 points 4 months ago

Workarounds for those sorts of limitations have been developed, though. Chain-of-thought prompting has been around for a while now, and I recall recently seeing an article about a model that had that built right into it; it had been trained to use tags to enclose invisible chunks of its output that would be hidden from the end user but would be used by the AI to work its way through a problem. So if you asked it whether cats had feathers it might respond "Feathers only grow on birds and dinosaurs. Cats are mammals. No, cats don't have feathers." And you'd only see the latter bit. It was a pretty neat approach to improving LLM reasoning.