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Understanding Reasoning LLMs

magazine.sebastianraschka.com

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Re: Understanding Reasoning LLMs

#41

Post by martin-t »

blovescoffee wrote: post #18
aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

The other day I fed a complicated engineering doc for an architectural proposal at work into R1. I incorporated a few great suggestions into my work. Then my work got reviewed very positively by a large team of senior/staff+ engineers (most with experience at FAANG; ie credibly solid engineers). R1 was really useful! Sorry you don’t like it but I think it’s unfair to say it sucks at reasoning.

[flagged]

Re: Understanding Reasoning LLMs

#43

Post by mlinsey »

bloomingkales wrote: post #31
danielmarkbruce wrote: post #27

Earlier quoted context omitted.

anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.

There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.

There are a couple Twitter personalities that definitely fit this description.

There is also a much bigger group of people that haven't really tried anything beyond GPT-3.5, which was the best you could get without paying a monthly subscription for a long time. One of the biggest reasons for r1 hype, besides the geopolitical angle, was people could actually try a reasoning model for free for the first time.

Re: Understanding Reasoning LLMs

#44

Post by codr7 »

_yb2s wrote: post #35
Jensson wrote: post #30

Earlier quoted context omitted.

> If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. You don't seem to understand how they work, they recurse their solution meaning if…

> You don't seem to understand how they work I don't think anyone understands how they work- these type of explanations aren't very complete or accurate. Such explanations/models allow one to reason out what types of things they should be capable of vs incapable of in principle regardless of scale or algorithm tweaks, and those predictions and arguments never match reality and require constant goal post shifting as t…

Well we do know pretty much exactly what they do, don't we?

What surprises us is the behaviors coming out of that process.

But surprise isn't magic, magic shouldn't even be on the list of explanations to consider.

Re: Understanding Reasoning LLMs

#45

Post by energy123 »

aithrowawaycomm wrote: post #8

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wi…

This is basically a misrepresentation of that tweet.

Re: Understanding Reasoning LLMs

#46

Post by ggm »

bloomingkales wrote: post #31
danielmarkbruce wrote: post #27

Earlier quoted context omitted.

anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.

There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.

Firstly this is meta ad hom. You're ignoring the argument to target the speaker(s)

Secondly, you're ignoring the fact that the community of voices with experience in data sciences, computer science and artificial intelligence themselves are split on the qualities or lack of them in current AI. GPT and LLM are very interesting but say little or nothing to me of new theory of mind, or display inductive logic and reasoning, or even meet the bar for a philosophers cave solution to problems. We've been here before so many, many times. "Just a bit more power captain" was very strong in connectionist theories of mind. fMRI brains activity analytics, you name it.

So yes. There are a lot of "us" who are pushing back on the hype, and no we're not a mini cult.

Re: Understanding Reasoning LLMs

#47

Post by gibsonf1 »

tmnvdb wrote: post #28
gibsonf1 wrote: post #13

There are no LLMs that reason, its an entirely different statistical process as compared to human reasoning.

"There are no LLMS that reason" is a claim about language, namely that the word 'reason' can only ever be applied to humans.

Not at all, we are building conceptual reasoning machines, but it is an entirely different technology than GPT/LLM dl/ml etc. [1]

[1] https://graphmetrix.com/trinpod-server

Re: Understanding Reasoning LLMs

#48

Post by jamiek88 »

danielmarkbruce wrote: post #33
bloomingkales wrote: post #31

Earlier quoted context omitted.

There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.

ie, the people that AI is dumb? Or you are saying I'm in a cult for being pro it - I'm definitely part of that cult - the "we already have agi and you have to contort yourself into a pretzel to believe otherwise" cult. Not sure if there is a leader though.

You think we have AGI? What makes you think that?

Re: Understanding Reasoning LLMs

#49

Post by ggm »

danielmarkbruce wrote: post #27
_yb2s wrote: post #22

Earlier quoted context omitted.

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland probl…

anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.

A good literary production. I would have been proud of it had I thought of it, but it's a path to observe a strong "whataboutery" element that if we use "stochastic parrot" as shorthand and you dislike the term, now you understand why we dislike the constant use of "infer", "reason" and "hallucinate"

Parrots are self aware, complex reasoning brains which can solve problems in geometry, tell lies, and act socially or asocially. They also have complex vocal chords and can perform mimicry. Very few aspects of a parrots behaviour are stochastic but that also underplays how complex stochastic systems can be in their production. If we label LLM products as Stochastic Parrots it does not mean they like cuttlefish bones or are demonstrably modelled by Markov chains like Mark V Shaney.

Re: Understanding Reasoning LLMs

#50

Post by layer8 »

codr7 wrote: post #44
_yb2s wrote: post #35

Earlier quoted context omitted.

> You don't seem to understand how they work I don't think anyone understands how they work- these type of explanations aren't very complete or accurate. Such explanations/models allow one to reason out what types of things they should be capable of vs incapable of in principle regardless of scale or algorithm tweaks, and those predictions and arguments never match reality and require constant goal post shifting as t…

Well we do know pretty much exactly what they do, don't we? What surprises us is the behaviors coming out of that process. But surprise isn't magic, magic shouldn't even be on the list of explanations to consider.

Magic wasn’t mentioned here. We don’t understand the emerging behavior, in the sense that we can’t reason well about it and make good predictions about it (which would allow us to better control and develop it).

This is similar to how understanding chemistry doesn’t imply understanding biology, or understanding how a brain works.

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