this post was submitted on 17 Dec 2024
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[–] spankmonkey@lemmy.world 41 points 1 day ago (4 children)

"Almost nothing" is not the same as "actually useless". The former is saying the applications are limited, which is true.

LLMs are fine for fictional interactions, as in things that appear to be real but aren't. They suck at anything that involves being reliably factual, which is most things including all the stupid places LLMs and other AI are being jammed in to despite being consistely wrong, which tech bros love to call hallucinations.

They have LIMITED applications, but are being implemented as useful for everything.

[–] Amoeba_Girl@awful.systems 25 points 1 day ago* (last edited 1 day ago) (3 children)

To be honest, as someone who's very interested in computer generated text and poetry and the like, I find generic LLMs far less interesting than more traditional markov chains because they're too good at reproducing clichés at the exclusion of anything surprising or whimsical. So I don't think they're very good for the unfactual either. Probably a homegrown neural network would have better results.

[–] dgerard@awful.systems 14 points 1 day ago (1 children)

GPT-2 was peak LLM because it was bad enough to be interesting, it was all downhill from there

[–] Amoeba_Girl@awful.systems 10 points 1 day ago

Absolutely, every single one of these tools has got less interesting as they refine it so it can only output the platonic ideal of kitsch.

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