I'm looking for an article showing that LLMs don't know how they work internally
I'm looking for an article showing that LLMs don't know how they work internally
I found the aeticle in a post on the fediverse, and I can't find it anymore.
The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.
Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.
This showed 2 things:
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LLM don't "know" how they work
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the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation
I think it was a very interesting an meaningful analysis
Can anyone help me find this?
EDIT: thanks to @theunknownmuncher @lemmy.world https://www.anthropic.com/research/tracing-thoughts-language-model its this one
EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095
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
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.
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
Thank you. I found the article, linkin the OP
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"
The environmental toll doesn’t have to be that bad. You can get decent results from single high-end gaming GPU.
You can, but the stuff that’s really useful (very competent code completion) needs gigantic context lengths that even rich peeps with $2k GPUs can’t do. And that’s ignoring the training power and hardware costs to get the models.
Techbros chasing VC funding are pushing LLMs to the physical limit of what humanity can provide power and hardware-wise. Way less hype and letting them come to market organically in 5/10 years would give the LLMs a lot more power efficiency at the current context and depth limits. But that ain’t this timeline, we just got VC money looking to buy nuclear plants and fascists trying to subdue the US for the techbro oligarchs womp womp
Who has claimed that LLMs have the capacity to reason?
More than enough people who claim to know how it works think it might be "evolving" into a sentient being inside it's little black box. Example from a conversation I gave up on... https://sh.itjust.works/comment/18759960
Oh wow thank you! That's it!
I didn't even remember now good this article was and how many experiments it collected
Here's a book for a different audience. Explains in layman terms why to be wary about this tech, https://thebullshitmachines.com/
I don't know how I work. I couldn't tell you much about neuroscience beyond "neurons are linked together and somehow that creates thoughts". And even when it comes to complex thoughts, I sometimes can't explain why. At my job, I often lean on intuition I've developed over a decade. I can look at a system and get an immediate sense if it's going to work well, but actually explaining why or why not takes a lot more time and energy. Am I an LLM?
I agree. This is the exact problem I think people need to face with nural network AIs. They work the exact same way we do. Even if we analysed the human brain it would look like wires connected to wires with different resistances all over the place with some other chemical influences.
I think everyone forgets that nural networks were used in AI to replicate how animal brains work, and clearly if it worked for us to get smart then it should work for something synthetic. Well we've certainly answered that now.
Everyone being like "oh it's just a predictive model and it's all math and math can't be intelligent" are questioning exactly how their own brains work. We are just prediction machines, the brain releases dopamine when it correctly predicts things, it self learns from correctly assuming how things work. We modelled AI off of ourselves. And if we don't understand how we work, of course we're not gonna understand how it works.
LLMs among other things lack the whole neurotransmitter "live" regulation aspect and plasticity of the brain.
We are nowhere near a close representation of actual brains. LLMs to brains are like a horse carriage compared to modern cars. Yes they have four wheels and they move, and cars also need four wheels and move, but that is far from being close to each other.
Even if LLM "neurons" and their interconnections are modeled to the biological ones, LLMs aren't modeled on human brain, where a lot is not understood.
The first thing is that how the neurons are organized is completely different. Think about the cortex and the transformer.
Second is the learning process. Nowhere close.
The fact explained in the article about how we do math, through logical steps while LLMs use resemblance is a small but meaningful example. And it also shows that you can see how LLMs work, it's just very difficult
You're definitely overselling how AI works and underselling how human brains work here, but there is a kernel of truth to what you're saying.
Neural networks are a biomimicry technology. They explicitly work by mimicking how our own neurons work, and surprise surprise, they create eerily humanlike responses.
The thing is, LLMs don't have anything close to reasoning the way human brains reason. We are actually capable of understanding and creating meaning, LLMs are not.
So how are they human-like? Our brains are made up of many subsystems, each doing extremely focussed, specific tasks.
We have so many, including sound recognition, speech recognition, language recognition. Then on the flipside we have language planning, then speech planning and motor centres dedicated to creating the speech sounds we've planned to make. The first three get sound into your brain and turn it into ideas, the last three take ideas and turn them into speech.
We have made neural network versions of each of these systems, and even tied them together. An LLM is analogous to our brain's language planning centre. That's the part that decides how to put words in sequence.
That's why LLMs sound like us, they sequence words in a very similar way.
However, each of these subsystems in our brains can loop-back on themselves to check the output. I can get my language planner to say "mary sat on the hill", then loop that through my language recognition centre to see how my conscious brain likes it. My consciousness might notice that "the hill" is wrong, and request new words until it gets "a hill" which it believes is more fitting. It might even notice that "mary" is the wrong name, and look for others, it might cycle through martha, marge, maths, maple, may, yes, that one. Okay, "may sat on a hill", then send that to the speech planning centres to eventually come out of my mouth.
Your brain does this so much you generally don't notice it happening.
In the 80s there was a craze around so called "automatic writing", which was essentially zoning out and just writing whatever popped into your head without editing. You'd get fragments of ideas and really strange things, often very emotionally charged, they seemed like they were coming from some mysterious place, maybe ghosts, demons, past lives, who knows? It was just our internal LLM being given free rein, but people got spooked into believing it was a real person, just like people think LLMs are people today.
In reality we have no idea how to even start constructing a consciousness. It's such a complex task and requires so much more linking and understanding than just a probabilistic connection between words. I wouldn't be surprised if we were more than a century away from AGI.
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.
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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
Yes, that's it. I added the link in the OP,
"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.
Very arrogant answer. Good that you have intuition, but the article is serious, especially given how LLMs are used today. The link to it is in the OP now, but I guess you already know everything...