Researchers at Apple have come out with a new paper showing that large language models can’t reason — they’re just pattern-matching machines. [arXiv, PDF] This shouldn’t be news to anyone here. We …
This isn’t news. We’ve known this for many, many years. It’s one of the reasons why many companies didn’t bother using LLM’s in the first place, that paired with the sheer amount of hallucinations you’ll get that’ll often utterly destroy a company’s reputation (lol Google).
With that said, for commercial services that use LLM’s, it’s absolutely not true. The models won’t reason, but many will have separate expert agents or API endpoints that it will be told to use to disambiguate or better understand what is being asked, what context is needed, etc.
It’s kinda funny, because many AI bros rave about how LLM’s are getting super powerful, when in reality the real improvements we’re seeing is in smaller models that teach a LLM about things like Personas, where to seek expert opinion, what a user “might” mean if they misspell something or ask for something out of context, etc. The LLM’s themselves are only slightly getting better, but the thing that preceded them is propping them up to make them better
IMO, LLM’s are what they are, a good way to spit information out fast. They’re an orchestration mechanism at best. When you think about them this way, every improvement we see tends to make a lot of sense. The article is kinda true, but not in the way they want it to be.
Are they a serious researcher in ML with insights into some of the most interesting and complicated intersections of computer science and analytical mathematics, or a promptfondler that earns 3x the former’s salary for a nebulous AI startup that will never create anything of value to society? Read on to find out!
“sigh”
(Preface: I work in AI)
This isn’t news. We’ve known this for many, many years. It’s one of the reasons why many companies didn’t bother using LLM’s in the first place, that paired with the sheer amount of hallucinations you’ll get that’ll often utterly destroy a company’s reputation (lol Google).
With that said, for commercial services that use LLM’s, it’s absolutely not true. The models won’t reason, but many will have separate expert agents or API endpoints that it will be told to use to disambiguate or better understand what is being asked, what context is needed, etc.
It’s kinda funny, because many AI bros rave about how LLM’s are getting super powerful, when in reality the real improvements we’re seeing is in smaller models that teach a LLM about things like Personas, where to seek expert opinion, what a user “might” mean if they misspell something or ask for something out of context, etc. The LLM’s themselves are only slightly getting better, but the thing that preceded them is propping them up to make them better
IMO, LLM’s are what they are, a good way to spit information out fast. They’re an orchestration mechanism at best. When you think about them this way, every improvement we see tends to make a lot of sense. The article is kinda true, but not in the way they want it to be.
Are they a serious researcher in ML with insights into some of the most interesting and complicated intersections of computer science and analytical mathematics, or a promptfondler that earns 3x the former’s salary for a nebulous AI startup that will never create anything of value to society? Read on to find out!
do i have to
Welcome to the future! Suffering is mandatory!
as a professional abyss-starer, I’m going to talk to my union about this
Preface: repent for your sins in sackcloth and ashes.
Buh bye now.
while true; do fortune; doneis a good way to spit information out fast.this but with extra wasabi
*trying desperately not to say the thing* what if AI could automatically… round out… spelling
It hurts when they’re so close!