You have understood correctly. What LLMs are, at least in their current state, is not fundamentally different from a simple markov chain generator.
Technically speaking, it is of course, far more complex. There is some incredible vector math and token rerouting going on; But in terms of how you get output from input - it's still "how often have I seen x in relation to y" at the core level.
They do not learn, they do not think, they do not reason. They are probability engines. If anyone tells you their LLM is not, it has just been painted over in snake oil to appear otherwise.
For all that I agree there's a big pile of snake oil in this area, I disagree with you overall.
Having played with Markov models since I was a kid*, LLMs are really not just that.
All that stuff you acknowledge but then gloss over, that is the actual learning, which tells it which previous tokens are relevant and how much attention to pay to them. This learning creates a world model which is functionally (barely but functionally) performing something approximately reasoning.
Statistics and probability is the mechanism it uses to do this, but that alone doesn't make it a Markov chain, those are a very specific thing that's a lot more limited.
For example: consider a context window of 128k tokens where each token has 64k possible discrete values. If implemented as a Markov chain, this would need a transition matrix of (2^16)^(2^17) by (2^16) entries (unless I've gotten one of the numbers backwards, but you get the idea regardless). This is too many, and because it is too many you have to create a function to approximate those transitions. But even then, that only works as a Markov chain if it's a deterministic function, and the actual behaviour is not deterministic due to the temperature setting not (usually) being zero.
* Commodore 64 user guide aged 6 or so, so I didn't really understand it at the time but that's what it was
Off the top of my head and 34 years later, sadly not.
Best I can do is describe this code/listing:
It created a few lists of words (IIRC these lists were adjectives, nouns, verbs?), and in the main loop it kept track of which list it had just taken a word from in order to decide what to pick next — e.g. if it had just picked an adjective then was allowed to pick either another adjective or go onto a noun, if it had just picked a noun then it could end the sentence or it could go on to a verb — and it would pick a random word from whichever list.
This either fit entirely on one screen, or very close to that — I was little, I would have made a syntax error if it had been much longer.
(Perhaps it will turn out to be my family's user manual wasn't even the official one, though I do remember it being a thick blue thing which matches the pictures I've seen online).
You can't write software code without the ability to think. You can't tactfully respond to emotional expression without the ability to think. If that is snake oil then we are all walking, talking, snake oil.
The way we do it is not the same way they do it. They literally only predict the next probable tokens. The way they do it is amazing, and the fact that they can do it as well as they do is amazing, but human thinking is a lot more nuanced than just predicting.
The fact that AI seems to be so reasoned is not that they are doing reasoning, but because there is a phenomenal amount of reasoning inherently embedded in their training data.
AI actually thinks in the same way that the figures on a movie screen actually move. It's a trick, and the difference may be pedantic, but it's very important in order to have a real discussion about the ramifications of it.
As far as I know we don't know how we do it. We have very little clue how our higher level behaviours emerge. So you can't claim we don't do it the same way.
Of course I can, humans learn faster from far less data and don't hallucinate to the same extent. What they do is very likely similar to a part of what we do, but they're missing critical components and my feeling is not all of them (empathy and creativity for example) are even possible to replicate outside of a human experience.
You are extrapolating from a result to the implementation and making a judgment call it's thus not the same. That is not valid to do. You can come up with countless examples of the same tech underlying principle beeing used but the results are dramatically better now. Lithography for example.
You could also look at a koala and make the argument they function totally differently from us since they almost can't learn anything and are extremely stupid.
You can clearly see behaviourol pattern in people and in their parents.
For example the boy who brushes his teeth the same way his father does.
I'm really lost on what you think your brain is doing? Have you never thought through things but acted differently? Like procrastination? Spouting out something and thinking after "ah man i should have just done x instead of y'?
If there are 8 blue beads and 2 red beads in a jar, and I ask the computer to draw a bead out of the jar and its a blue one that it has drawn, did it really think about giving me the bead?
They’re not responding “tactfully”, you’re projecting emotion to a bunch of words written coldly.
It’s like writing a program that has a number of fixed strings like “I feel sad” or “I’m depressed” and when it sees those it outputs “I’m sorry to hear that. I’m here for you and love you”. The words may be comforting and come at the right time, but there’s no feeling or thought put into them.
Humans can measure feelings, computers can't. Therefore I can say if ChatGPT doesn't have enough feeling but it can never do the inverse to me.
That feels simplistic, but we're dealing with fundamentally human concepts. I see absolutely no reason to work under the assumption that computer programs are somehow in the same domain as human thought, which is what a lot of people (you) are saying.
The goal should not be to demonstrate ChatGPT and Humans are different, because to me that is obvious and should be the starting point. Rather we should do the inverse, show that ChatGPT is indistinguishable from a person, as measured by Humans. And then, maybe, we can consider granting this computer program human rights like the right to use copyrightable media in a transformative way.
Ah, but that is really hard to do. So the AI tech bros don't do it, and instead work in the opposite direction.
They learned already at the beginning. Its called training.
Its the same thing we humans do, just a lot faster and focused on the content we give it.
'think' what is thinking? Recalling what you learned?
Wiki says: "Their most paradigmatic forms are judging, reasoning, concept formation, problem solving, and deliberation. But other mental processes, like considering an idea, memory, or imagination, are also often included"
Talk to an LLM, it will reflect these concepts very well.
'reason': even people don't reason. I had plenty of discussions with people who do not act logical. And there have been plenty of good examples of LLMs leanring to reason. Look at Grok2 and just wait for GPT 5.
You put the achievement of LLM down as its nothing despite the fact that it could mean a lot more. How big is the chance that we are also just probaiblity engines?
We as humans are more individual than a LLM and we do have more mechanism in our brains like time components, emotional interactions, social systems.
And not even your mentioning of "Markov chain" is correct: A LLM Architecture is not how a markov chain works otherwise we wouldn't have the scaling issues we have with the LLMs...
Technically speaking, it is of course, far more complex. There is some incredible vector math and token rerouting going on; But in terms of how you get output from input - it's still "how often have I seen x in relation to y" at the core level.
They do not learn, they do not think, they do not reason. They are probability engines. If anyone tells you their LLM is not, it has just been painted over in snake oil to appear otherwise.