this post was submitted on 11 Oct 2024
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[–] JackGreenEarth@lemm.ee 32 points 1 month ago (5 children)

It's only a problem if you expect them to do formal reasoning. They are fancy word predictors, useful for when your output doesn't need to be factually accurate. If you use them for things they're not designed for, you'll get bad results, but that would be your fault for using them in an incorrect manner, not the LLMs' faults. You don't use a screwdriver to bang in a nail and say the screwdriver 'has a HUGE problem' when it does a bad job.

[–] yogthos@lemmy.ml 4 points 1 month ago (2 children)

Right, I find LLMs are fundamentally no different from Markov chains. It doesn't mean they're not useful, they're a tool that's good for certain use cases. Unfortunately, we're in a hype phase right now where people are trying to apply them for a lot of cases they're terrible at and where better tools already exist to boot.

[–] vrighter@discuss.tchncs.de 2 points 1 month ago (1 children)

they aren't. The only difference is that the state transition table is so unimaginably gargantuan thit we can only generate an approximation of a tiny slice of it, instead of it being literally a table

[–] yogthos@lemmy.ml 1 points 1 month ago
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