Max Leibman2d ago
I don't like the term "hallucinations" when we talk about AI. Sure, LLMs can get things wrong, but a hallucination is an error in perception, and you can't have an error in perception when there's no one there to perceive. The only hallucinations that are happening are on your side of the keyboard.
Show threadeuroplus 1d ago

@maxleibman @petrillic I had a realisation related to this a month ago.

Given the way all genAI outputs are generated, if one is a hallucination, they all are.

https://social.europlus.zone/@europlus/116191458412032034

europlus :autisminf: (@[email protected])

I’m sure many other have made this observation, but even just reading this post without reading the linked article made me realise (or remember) that... *All* LLM output is, in fact, a hallucination. Because the way it formulates a “hallucination” *is exactly the same* as how it formulates a response *we don’t consider* a hallucination. Same with “good” vs “bad” summaries (and whatever the relative occurrence of each is). #NoAI #HumanMade

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Apr 9 at 10:29pmWeb
Show threadChristian Kent   𝘊𝘒 :＼﹥1d ago

@maxleibman @europlus @petrillic yep if you run different LLMs at home and dumb them down to smaller faster models, that’s pretty much it

There are some interesting takes on how to quantify this stuff (easily and really quickly, even though there was an industry around benchmarking that turned up out of nowhere, complete with a glossary of jargon) and while I couldn’t find a way to do it myself a year ago, I saw something last week that was rather good. Alex Ziskind probably.