This article is not about "reasoning" in the abstract, philosophical sense but is talking about "mechanistic interpretability" research. The title is more like, "can we understand if the 'knowledge' encoded into a neural networks actually corresponds to reasoning-like concepts" and doing that with actual experiments like tweaking weights and activations.
There's an interesting example where researchers saw a model approached clock time calculations and calendar month-day calculations using the same methodology. So then is this because an underlying concept of "cyclical measures" has emerged in the network?
They dont. They have input that runs through a invisible stochastic canyon. As long as there is previous experience the stochastic canyon never ends. If there is none or isignificant one, or it runs out of tokkens, it hallucinates and the illusion falls apart. There is no reasoning, just the invisible grand canyon of all of human experience and knowledge. PS: try to get it to retell you a clichee movie or book and you can see life near the end, how the delta of all the same movies opens up into wildly different endings.
To advance further it would need the ability to abstract away the general situation shape and pattern recognize similar situations.
It's probably helpful in this discussion to make a difference between two definitions of reasoning:
1. phenomenal reasoning, requiring consciousness and subjective experience
2. functional reasoning, transforming premises into conclusions using logic
I think you are attacking this using definition 1, whereas the article is obviously aiming at a different type of reasoning, and trying to formalize what is actually going on. It seems to be a genuine effort.
Compression is the trick. Its even philosophed about if compression = intelligence.
The LLM has to compress everyy question/prompt into its system. It does so by creating rules and ways of processing data (this can lead to AGI, world models or an architecture of sub architectures like an LLM + something else). So if it should respond in a way that only reasoning people can achieve, it might be able to learn a representation of what we call reasoning.
It read enough text in itself to even know about the concept of reasoning and how you would do that.
Even if this is only stochastic, it shouldn't be so devalued as your comment comes across.
There is a streamer who plays Diablo 2 by listening to the AI advice and it is quite funny since it is pretty clear that most of the advice is an amalgamation of random, often incorrect advicem
I wonder if it is the same for programming or not, but I vibe coded an android app just to see if I can and it just works. It required a lot of "build the code and correct the errors" pushing though.
For example requested code in kotlin but received something else.
One plausible reason I thought of that we may not understand neural nets is that by their nature their power grows with ever-more complex connections and weights.
So it is like the opposite of logical systems, in that the very design of neural net architecture is a mess of parameter "spaghetti code" which renders the entire thing a metaphorical encrypted black box. The more powerful an AI/AGI the more this would be the case, and this is analogous a complexity curve.
And so any effort to make sense of such black box computation would be like trying to reverse entropy, analogous to trying to recover information lost in waste heat. And that could be one fundamental barrier to understanding both human and artificial brains alike, relative to their internal complexity.
(Just thinking aloud my handwavy pet theory recently, I am not an expert and could be totally mistaken on this)
> Do we see signs of reasoning or is it anthropomorphism?
This is the part that so many folks just don't seem to understand (probably because it's been labeled as "thinking" or "reasoning" mode, and people assume that words have meaning). It's not reasoning or thought. It's spewing tokens pretending to "think", but it's actually just generating extra "context" to help the final answer be more coherent. The model isn't doing anything it doesn't already do. It's just doing more of it to improve the quality of the final answer displayed to the user.
You're describing a process by which a 'thinking' entity uses cognition to refine a solution to a stated problem. That's a lot of words so usually we shorten this to 'reasoning'.
Do LLMs 'think'? I 'think' they do in a way. I don't really know how I think myself but I know I do and therefore I am (thanks, Descartes). I have a somewhat better grasp of the way LLMs 'think'. They do so sequentially, building a chain of descriptors which best fit the problem and the preceding descriptors. I suspect I do something not entirely dissimilar- i.e. I imagine 'worlds' which are like the current one changed in some way so they the problem I'm working on is reduced, then refine those until it is resolved - but in a massively parallel way.
Fine. Whatever. I give up. LLMs think. Believe what you want. I literally no longer care, and this argument is beyond exhausting. Go ask the LLM to explain itself to you. It will happily spew out a pretty solid explanation of the details and math involved if you ask it the right questions in the right way. It'll also happily play along with you if you want to roleplay that it is an actual thinking machine. It's designed that way. But hey, whatever. It's a thinking intelligent machine and we're all doomed. I accept that my many decades of working with and learning about computers was wasted and I know nothing about them at all.
Honestly, people need to get over this debate. It's pretty irrelevant in a lot of cases. When people ask "what is the model thinking?", they're really asking "what caused the model to produce this response (as opposed to a bunch of other plausible ones)?"
Whether it's thinking or word prediction or whatever you want to call it, people are trying to understand the causal chain.
It's not just a nominalistic debate though, as the people who are vocal against the idea that LLMs might "understand" or "think" also claim that because of this, they are fundamentally limited in what they can achieve, in contrast to human beings. Therefore any possibility of actual intelligence (or even superintelligence) is, according to them, just a fantasy.
The papers linked in the article discuss the mechanical operations that simulate reasoning. Intelligence is data efficiency and I don't see a strong argument that reasoning can exist if it requires a world's worth of data.
That doesn't mean that simulated reasoning isn't useful, it's wildly useful. But a thing is not its simulation.
Yes, there is an LLM feature that we have anthropomorphized as "reasoning" or "thinking", where an LLM has a scratch space where it can dump tokens that help to improve the final output.
Of course they do, how else do you think they manage to implement new features in large codebases, or to prove new theorems? But you don't even have to assume they do because of the results- you can read their chain of thought.
For the love of all that is sacred, please stop doing this. I'm begging you. The whole social media landscape is dying and you are creating a throwaway to participate in ruining this small corner. I assume this is not your first. And no one is convinced by this! The guidelines are there for your benefit as well. You achieve nothing but hastening the destruction of one of the last half-decent communities. Sorry for the melodrama.
The top two comments in this thread agree with the point you just made. This is true of essentially any thread on the subject. If this place sucks, it would have to be because of people like you. If not, you in particular may not be very good at noticing.
Well, if you read the foundational paper 'All you need is Attention', review the full stack trace of any LLM system call, and have insight to the ad hoc training process to ingest additional data and knowledge, you will gain greater understanding.
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There's an interesting example where researchers saw a model approached clock time calculations and calendar month-day calculations using the same methodology. So then is this because an underlying concept of "cyclical measures" has emerged in the network?
To advance further it would need the ability to abstract away the general situation shape and pattern recognize similar situations.
1. phenomenal reasoning, requiring consciousness and subjective experience
2. functional reasoning, transforming premises into conclusions using logic
I think you are attacking this using definition 1, whereas the article is obviously aiming at a different type of reasoning, and trying to formalize what is actually going on. It seems to be a genuine effort.
The LLM has to compress everyy question/prompt into its system. It does so by creating rules and ways of processing data (this can lead to AGI, world models or an architecture of sub architectures like an LLM + something else). So if it should respond in a way that only reasoning people can achieve, it might be able to learn a representation of what we call reasoning.
It read enough text in itself to even know about the concept of reasoning and how you would do that.
Even if this is only stochastic, it shouldn't be so devalued as your comment comes across.
Who says that we are doing anything more magic?
I wonder if it is the same for programming or not, but I vibe coded an android app just to see if I can and it just works. It required a lot of "build the code and correct the errors" pushing though. For example requested code in kotlin but received something else.
So it is like the opposite of logical systems, in that the very design of neural net architecture is a mess of parameter "spaghetti code" which renders the entire thing a metaphorical encrypted black box. The more powerful an AI/AGI the more this would be the case, and this is analogous a complexity curve.
And so any effort to make sense of such black box computation would be like trying to reverse entropy, analogous to trying to recover information lost in waste heat. And that could be one fundamental barrier to understanding both human and artificial brains alike, relative to their internal complexity.
(Just thinking aloud my handwavy pet theory recently, I am not an expert and could be totally mistaken on this)
The article body does not presume they reason.
We see some signs of reasoning, but also we understand little about how they work.
This is the part that so many folks just don't seem to understand (probably because it's been labeled as "thinking" or "reasoning" mode, and people assume that words have meaning). It's not reasoning or thought. It's spewing tokens pretending to "think", but it's actually just generating extra "context" to help the final answer be more coherent. The model isn't doing anything it doesn't already do. It's just doing more of it to improve the quality of the final answer displayed to the user.
Do LLMs 'think'? I 'think' they do in a way. I don't really know how I think myself but I know I do and therefore I am (thanks, Descartes). I have a somewhat better grasp of the way LLMs 'think'. They do so sequentially, building a chain of descriptors which best fit the problem and the preceding descriptors. I suspect I do something not entirely dissimilar- i.e. I imagine 'worlds' which are like the current one changed in some way so they the problem I'm working on is reduced, then refine those until it is resolved - but in a massively parallel way.
Whether it's thinking or word prediction or whatever you want to call it, people are trying to understand the causal chain.
Yes, we have a tendency to anthropomorphize, but (most) researchers are aware of this.
That doesn't mean that simulated reasoning isn't useful, it's wildly useful. But a thing is not its simulation.
Do they actually help? Are you sure?
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