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"Researchers" did a thing I did the first day I was actually able to ChatGPT and came to a conclusion that is in the disclaimers on the ChatGPT website. Can I get paid to do this kind of "research?" If you've even read a cursory article about how LLMs work you'd know that asking them what their reasoning is for anything doesn't work because the answer would just always be an explanation of how LLMs work generally.
Oh wow thank you! That's it!
I didn't even remember now good this article was and how many experiments it collected
Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):
LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.
Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.
In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.
If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.
It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.
TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster
People don't understand what "model" means. That's the unfortunate reality.
I've read that article. They used something they called an "MRI for AIs", and checked e.g. how an AI handled math questions, and then asked the AI how it came to that answer, and the pathways actually differed. While the AI talked about using a textbook answer, it actually did a different approach. That's what I remember of that article.
But yes, it exists, and it is science, not TicTok
How would you prove that someone or something is capable of reasoning or thinking?
It's a developer option that isn't generally available on consumer-facing products. It's literally just a debug log that outputs the steps to arrive at a response, nothing more.
It's not about novel ideation or reasoning (programmatic neural networks don't do that), but just an output of statistical data that says "Step was 90% certain, Step 2 was 89% certain...etc"
It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.
Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.
EDIT: lol you can downvote me but it doesn't change evidence based research
It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.
Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.
Too deep on the AI propaganda there, it’s completing the next word. You can give the LLM base umpteen layers to make complicated connections, still ain’t thinking.
The LLM corpos trying to get nuclear plants to power their gigantic data centers while AAA devs aren’t trying to buy nuclear plants says that’s a straw man and you simultaneously also are wrong.
Using a pre-trained and memory-crushed LLM that can run on a small device won’t take up too much power. But that’s not what you’re thinking of. You’re thinking of the LLM only accessible via ChatGPT’s api that has a yuge context length and massive matrices that needs hilariously large amounts of RAM and compute power to execute. And it’s still a facsimile of thought.
It’s okay they suck and have very niche actual use cases - maybe it’ll get us to something better. But they ain’t gold, they ain't smart, and they ain’t worth destroying the planet.
By design, they don't know how they work. It's interesting to see this experimentally proven, but it was already known. In the same way the predictive text function on your phone keyboard doesn't know how it works.
I'm aware of this and agree but:
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I see that asking how an LLM got to their answers as a "proof" of sound reasoning has become common
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this new trend of "reasoning" models, where an internal conversation is shown in all its steps, seems to be based on this assumption of trustable train of thoughts. And given the simple experiment I mentioned, it is extremely dangerous and misleading
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take a look at this video: https://youtube.com/watch?v=Xx4Tpsk_fnM : everything is based on observing and directing this internal reasoning, and these guys are computer scientists. How can they trust this?
So having a good written article at hand is a good idea imho
I only follow some YouTubers like Digital Spaceport but there has been a lot of progress from years ago when LLM's were only predictive. They now have an inductive engine attached to the LLM to provide logic guard rails.
Define "know".
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An LLM can have text describing how it works and be trained on that text and respond with an answer incorporating that.
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LLMs have no intrinsic ability to "sense" what's going on inside them, nor even a sense of time. It's just not an input to their state. You can build neural-net-based systems that do have such an input, but ChatGPT or whatever isn't that.
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LLMs lack a lot of the mechanisms that I would call essential to be able to solve problems in a generalized way. While I think Dijkstra had a valid point:
The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.
...and we shouldn't let our prejudices about how a mind "should" function internally cloud how we treat artificial intelligence...it's also true that we can look at an LLM and say that it just fundamentally doesn't have the ability to do a lot of things that a human-like mind can. An LLM is, at best, something like a small part of our mind. While extracting it and playing with it in isolation can produce some interesting results, there's a lot that it can't do on its own: it won't, say, engage in goal-oriented behavior. Asking a chatbot questions that require introspection and insight on its part won't yield interesting result, because it can't really engage in introspection or insight to any meaningful degree. It has very little mutable state, unlike your mind.
There was a study by Anthropic, the company behind Claude, that developed another AI that they used as a sort of "brain scanner" for the LLM, in the sense that allowed them to see sort of a model of how the LLM "internal process" worked