this post was submitted on 11 Aug 2025
137 points (99.3% liked)

Technology

43360 readers
221 users here now

A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.

Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.

Subcommunities on Beehaw:


This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.

founded 4 years ago
MODERATORS
 

Using supervised fine-tuning (SFT) to introduce even a small amount of relevant data to the training set can often lead to strong improvements in this kind of "out of domain" model performance. But the researchers say that this kind of "patch" for various logical tasks "should not be mistaken for achieving true generalization. ... Relying on SFT to fix every [out of domain] failure is an unsustainable and reactive strategy that fails to address the core issue: the model’s lack of abstract reasoning capability."

Rather than showing the capability for generalized logical inference, these chain-of-thought models are "a sophisticated form of structured pattern matching" that "degrades significantly" when pushed even slightly outside of its training distribution, the researchers write. Further, the ability of these models to generate "fluent nonsense" creates "a false aura of dependability" that does not stand up to a careful audit.

As such, the researchers warn heavily against "equating [chain-of-thought]-style output with human thinking" especially in "high-stakes domains like medicine, finance, or legal analysis." Current tests and benchmarks should prioritize tasks that fall outside of any training set to probe for these kinds of errors, while future models will need to move beyond "surface-level pattern recognition to exhibit deeper inferential competence," they write.

all 29 comments
sorted by: hot top controversial new old
[–] panda_abyss@lemmy.ca 18 points 1 year ago* (last edited 1 year ago)

Chain of thought is basically garbage.

It works with coding agents because they get an automated hard failure.

The rest of the time it’s just sampling the latent space around a response and should be trimmed out.

That could work with diffusion models but autoregresive models it’s just polluting the context window with the hopes of finding longer tail tokens.

[–] teawrecks@sopuli.xyz 14 points 1 year ago (1 children)

The analogy I use is, it's like a magician pulled a coin from behind a CEO's ear, and their response was "that's incredible! Free money! Let's go into business together!"

Literally no one ever claimed it had reasoning capabilities. It is a trick to produce a string of characters that your brain can make sense of. That's all.

[–] anachronist@midwest.social 7 points 1 year ago (2 children)

Literally no one ever claimed it had reasoning capabilities

Altman and similar grifters were and are absolutely making those claims but maybe we're excusing them as obvious liars?

[–] TehPers@beehaw.org 5 points 1 year ago

They are obvious liars. Some people are just too invested to see it.

These models only have reasoning capabilities using the most obscure definitions of "reasoning". At best, all they're doing are climbing to local maxima with their so-called "reasoning" on a graph as wavy as the ocean.

I've mentioned this on other posts, but it's really sad because LLMs have been wildly incredible for certain NLP operations. They are that though, not AGI or whatever snake oil Altman wants to sell this week.

[–] teawrecks@sopuli.xyz 2 points 1 year ago

The CEOs you're talking about are the CEOs in the analogy.

[–] jarfil@beehaw.org 8 points 1 year ago* (last edited 1 year ago) (3 children)

chain-of-thought models

There are no "CoT LLMs", a CoT means externally iterating an LLM. The strength of CoT, resides in its ability to pull up external resources at each iteration, not in dogfooding the LLM its own outputs.

"Researchers" didn't "find out" this now, it was known from day one.

As for who needs to hear it... well, apparently people unable to tell apart an LLM from an AI.

[–] CanadaPlus@lemmy.sdf.org 5 points 1 year ago

Yes, but it supports the jerk that everything called or associated with AI is bad, so it makes a popular Beehaw post.

[–] RoadTrain 2 points 1 year ago

a CoT means externally iterating an LLM

Not necessarily. Yes, a chain of thought can be provided externally, for example through user prompting or another source, which can even be another LLM. One of the key observations behind these models commonly referred to as reasoning is that since an external LLM can be used to provide "thoughts", could an LLM provide those steps itself, without depending on external sources?

To do this, it generates "thoughts" around the user's prompt, essentially exploring the space around it and trying different options. These generated steps are added to the context window and are usually much larger that the prompt itself, which is why these models are sometimes referred to as long chain-of-thought models. Some frontends will show a summary of the long CoT, although this is normally not the raw context itself, but rather a version that is summarised and re-formatted.

[–] interdimensionalmeme@lemmy.ml 1 points 1 year ago

I think of chain of thought as a self-prompting model
I suspect in the future, chain-of-thought model will run
a smaller tuned/dedicated chain-of-thought submodel just for the chain-of-thought tokens

The point of this is that, most users aren't very good at
prompting, they just don't have the feel for it

Personally I get worse results, way less what I wanted,
when CoT is enabled, I'm very annoyed that now
the "chatgpt classic" model selector just decides to use CoT
whenever it wants, I should be the one to decide that
and I want it off almost all of the time !!

[–] troyunrau@lemmy.ca 4 points 1 year ago (1 children)

Admittedly, a lot of people have flawed reasoning too.

[–] Catoblepas@piefed.blahaj.zone 15 points 1 year ago (1 children)

The difference being that people do reason, while LLMs don’t.

[–] BlameThePeacock@lemmy.ca 1 points 1 year ago (1 children)

I've met far too many people I wouldn't trust to give me a reasoned response.

Some people simply lack that capacity entirely, some just don't care enough to spend the effort on it, while others are trying to deceive me intentionally.

[–] Catoblepas@piefed.blahaj.zone 11 points 1 year ago (1 children)

LLMs are incapable of reasoning. There is not a consciousness in there deciding and telling you things. My comment was entirely about whether LLMs can reason, not whether all people reason at the same level or might decide to trick you.

[–] BlameThePeacock@lemmy.ca 2 points 1 year ago (1 children)

I don't disagree with you that LLMs don't reason. I disagree that all Humans can or do reason.

[–] TehPers@beehaw.org 5 points 1 year ago

I disagree that all Humans can or do reason.

Well if we're talking about all humans...

But more seriously, it doesn't take much looking to find someone who doesn't reason. Just look on the TV during the next major election and you'll find a bunch.