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.

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.

Who says that we are doing anything more magic?

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.

Stochastic gradient descent can be likened to traveling down a billion-dimensional canyon. But inference? Hardly.

It’s curious how they solve unsolved math problems without reasoning. Maybe I have a different definition of reasoning than you.

Guess what? SAT solvers have also solved unsolved math problems. Do you believe they are “reasoning”?

My toaster doesn't reason, and neither do the current clankers.

there's a 2MP about the related paper: https://www.youtube.com/watch?v=l72ufA-4SzE

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 LLMs have Qualia?

Do people?

Yes.

How do you know?

They don't reason.

Clickbait article title.

The article body does not presume they reason.

Do they ?

The article answers this question, at least to the extent it can be answered, at this time.

We see some signs of reasoning, but also we understand little about how they work.

Do we see actual signs of reasoning or is it anthropomorphism? We have an innate tendency to do so as humans.

> 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.

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.

Angry diatribes about whether submarines swim or not.

Yes, we do see signs of actual reasoning, see the papers linked in the article. (There are many others too.)

Yes, we have a tendency to anthropomorphize, but (most) researchers are aware of this.

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.

> that help to improve the final output

Do they actually help? Are you sure?

It's indeed so powerful that even my compiler and my unit tests fell victim of this delusion.

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.