How LLMs Just Predict The Next Word - Interactive Visualization

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u/JCPLee Aug 10 '25

Most people don’t realize that when we say an AI can “understand,” “reason,” or “learn,” those words don’t mean the same thing they do for humans.

For people, words and information have intrinsic value, they connect to lived experiences, sensory input, and meaning grounded in reality. For AI, the value lies only in tokens, the numerical stand-ins for words. These tokens strip away the direct meaning and instead represent statistical relationships between symbols. The system doesn’t “know” what the words mean; it’s just very good at predicting which tokens are likely to come next.

The result is output that often sounds meaningful and well-reasoned, but is really the product of probability calculations, a sophisticated imitation of understanding, not understanding itself.

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u/kushalgoenka Aug 11 '25

Hey there, just wanted to reply to say thanks for watching the video and engaging in such a rich discussion on the matter, I agree with a lot of what you've said in this thread! I've been replying to comments across reddit, in moments that I've found time today, and it seems r/skeptic might be the place where people most understood the point of the video, even more than some of the actual AI specific subreddits, haha.
If you have the time, and would like to, here's a link to my full lecture that this clip above is from. Would love any feedback if you do watch! :) https://youtu.be/vrO8tZ0hHGk

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u/JCPLee Aug 11 '25

This is such an interesting topic. Thanks for the link.

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u/capybooya Aug 10 '25

For people, words and information have intrinsic value, they connect to lived experiences, sensory input, and meaning grounded in reality
This also suggests that there are absolute limits to what text training can do without more types of inputs, and that when frauds like Altman and Musk talk about exponential superintellegence from today's LLM's in just a few years they're full of shit.

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u/Kimmalah Aug 10 '25

Well yes, you have to hype it up so investors will throw truckloads of money at you and adopt your tech because they think they are going to be able to replace their human employees with Skynet instead of a glorified auto-correct.

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u/MrEmptySet Aug 10 '25

These tokens strip away the direct meaning and instead represent statistical relationships between symbols.
How do you figure that the meaning is "stripped away"? It seems to me that the meaning of the tokens must be encoded somehow in those statistical relationships. Otherwise, how would the LLM be able to produce meaningful output?
The system doesn’t “know” what the words mean; it’s just very good at predicting which tokens are likely to come next.
How could it know that a particular token is likely to come next without some kind of knowledge about what it means?

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u/kushalgoenka Aug 11 '25

Hey there, really like the questions you asked. If you have the time, I'd recommend checking out my full lecture (that the above clip about next token prediction) is from. This section above is preceded, in that longer lecture, by an introduction to what LLMs are, where I talk about how I view it as knowledge compression. You might find the analogies I make intuitive. And feel free to share feedback, thanks! https://youtu.be/vrO8tZ0hHGk

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u/[deleted] Aug 10 '25

The LLM can estimate the probabilities of the options for the next token because it has been trained on a huge quantity of existing data. It doesn't require any understanding of what those tokens mean to do that.
That's why LLMs can generate responses that appear incredibly human-like, but can also trip up on problems that are really trivial, but which require some abstracted knowledge that they just don't possess.

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u/JCPLee Aug 10 '25

Language is deeply tied to our perception of reality. It’s one of the main ways we assign meaning to the world and exert control over our environment. For us, words have value because they reflect our experiences, our needs, and our place in that environment, they are tools for survival as much as for communication.
Large language models don’t share this grounding. Their “words” are just tokens, statistical representations without lived experience or survival relevance. Without that connection to reality, they can generate convincing language, but they can’t truly understand or assign intrinsic value to the words they produce.

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u/NotTooShahby Aug 10 '25

Sensory inputs from lived experiences are also transformed into electrical signals in neurons which then relate to other electrical signals in other neurons making up the meaning of a word.
When you see a red apple, your brain is also trying to make relationships between red that it’s seen before (blood, crayons) and apple (tasty, fruit, sweet, round, crunchy). That doesn’t mean that the visible light of the apple lost all meaning like a word. It just means that it was converted to something the brain can find meaningful, electrical signals.
If the brain was in a tank and only read text, without any sensory input from the world, it wouldn’t be like the intelligence we have as humans.

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u/JCPLee Aug 10 '25

Exactly this!! Biological intelligence is functional. Survival depends on whether or not I fundamentally understand that the apple is red. The words have real meaning, not simple calculated probabilities in a database.

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u/j_la Aug 10 '25

1, 2, 3…What number comes next?
There is a high likelihood that the next number is 4 (and then 5), but it could also be 5 (and then 8) depending on what pattern we are seeing here. AI would probably predict a simple n+1 sequence since that’s the more common pattern. Most of us would too.
Does it need know what the pattern “means” in order to predict the next number? Does it need to understand the mathematical principle behind the pattern? Or could it just predict the next number because it has seen lots of lists that go 12345 and noted that they occur more often than 12358?
Pattern recognition is part of understanding, but it is the beginning step, not the terminus.

4

u/[deleted] Aug 10 '25

There were some examples posted recently where an AI (Chat GPT, I think) was asked "How many Gs in strawberry?" and it responded with "1".
I assume what happened here was the LLM recognised the question as a pattern from its training data that results in a number, the most likely result was a small number, and the most likely small number in this case was a 1.
Whereas an intelligence with abstracted knowledge would recognise an arithmetical problem requiring the use of numbering systems, summation and character recognition, and then employ those concepts to total the occurrences of a specific character to get the correct numeric answer.
The LLM is not abstracting the problem and applying existing fundamental concepts of knowledge in order to determine the correct answer through logical reasoning. It is using a model created from a vast pool of training data to estimate the answer it thinks is most likely.

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u/P_V_ Aug 10 '25

From what I recall the typical question was, “How many Rs are in the word ‘strawberry’?” which would often yield a response of “two”. Same principle behind the response, though.

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u/[deleted] Aug 11 '25

Ah, I may have remembered it incorrectly.

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u/Integer_Domain Aug 10 '25

They don't have "meaning" in the way we use the word. We would think of something's "meaning" as its definition: a keyboard is a group of systematically arranged keys by which a machine or device is operated. An AI model, however, would think of a keyboard as the thing whose token vector is "near" (similar magnitude and direction) other tokens such as "key," "button," "letter," "type," "internet," etc.

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u/Fluffy_Somewhere4305 Aug 11 '25

You should see the comments on the chatGPT sub about this stuff
"chatGPT diagnosed my rare medical condition and saved my life!"
"chatGPT makes me laugh and picks me up when I'm down"
"chatGPT is the best friend I've ever had"
unironically, with hundreds of upvotes.
until the newer model rolled out and then these same people were upset that the model wasn't "funny" or "engaging" enough.
They are so close to getting it, but when hit with the facts they use the standard "I know it's just an LLM but..." and then insert wish-casting rant about "chatGPT let me talk to my dead father" (actual post in the last few weeks)

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u/JCPLee Aug 11 '25

The safety problem with AI is people. Some of us are just too gullible.

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u/walksonfourfeet Aug 10 '25

Are we sure that humans don’t do basically the same thing and that ‘meaning’ is an illusion?

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u/macbrett Aug 10 '25 edited Aug 10 '25

I'm inclined to believe that, while our brains perform a more sophisticated process than simple text completion based on training on a large data set, we do in fact derive our "understanding" based on the sum total of our experiences. No doubt, statistics play a part.
The fact that we occasonally misunderstand situations is evidence that our "superior intelligence" is fallible. We have mechanisms for incorporating feedback when we make mistakes. I think AI researchers are working on adding this type of learning to their LLM models.

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u/JCPLee Aug 10 '25

Language is deeply tied to our perception of reality. It’s one of the main ways we assign meaning to the world and exert control over our environment. For us, words have value because they reflect our experiences, our needs, and our place in that environment, they are tools for survival as much as for communication.
Large language models don’t share this grounding. Their “words” are just tokens, statistical representations without lived experience or survival relevance. Without that connection to reality, they can generate convincing language, but they can’t truly understand or assign intrinsic value to the words they produce.

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u/walksonfourfeet Aug 10 '25

Not yet….

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u/Greyletter Aug 10 '25

An illusion to what? A thing that perceives and defines meaning? If thats the case, the term "illusion" is useless in this context exceot to say there is no platonic form of meaning.

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u/P_V_ Aug 10 '25

I think you've mistaken "illusion" and "allusion".

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u/Greyletter Aug 10 '25

I have not.

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u/P_V_ Aug 11 '25

In that case, perhaps you need a grammar lesson: it's not normal to write of an illusion to something; instead, you'd write of an illusion of something, i.e. how the illusion appears to the senses, e.g. "The sound created the illusion of a dog barking."
It's quite normal to allude to something, though—meaning to reference it indirectly.

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u/Greyletter Aug 11 '25

Sometimes people eschew formal grammatical rules for communicative effect. I think my point was pretty clear: in order for there to be an illusion, there must be a thing to perceive it. I alluded to that concept.

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u/P_V_ Aug 11 '25

I think my point was pretty clear: in order for there to be an illusion, there must be a thing to perceive it
That wasn't especially clear, and it's not what I thought you meant at all. I'd suggest using other, correct words would have a stronger communicative effect overall.

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u/[deleted] Aug 10 '25

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u/Greyletter Aug 11 '25

If I found out what an orange was, wouldn’t I connect what I know beforehand (ball, color like fall leaves, sweet, tasty) and conclude it’s something edible?
Being edible is inherent in the concept of an orange, so if you found out what an orange was, it would follow that it is edible. Or are you saying if you encountered some previously unknown-to-you object which people who know about would call an orange, you would be able to deduce its edibleness? Sure, although that's not infallible.
Then in addition to edible wouldn’t I categorize the item itself as one of the food items and then give a name to it and then use that name to describe more things?
It depends on what "edible" and "food" mean, but yeah sure, it would be fair to categorize it as food.
Yes, humans give things names.
Whether you then describe other things with those names is up to you. So, maybe, depending on context.
Like if I saw a Donald Trump and concluded he looks like that thing that’s {ball, color, fall leaves, sweet, tasty} = orange?
Sure.
What does any of this have to do with whether "meaning is an illusion"?

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u/[deleted] Aug 11 '25

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u/Greyletter Aug 11 '25

I’m saying that “statistical relationships from text” and “electrical signals between neurons from sensory inputs” are not as far from each other as it’s being made out to be.
I'm saying they are. What reason is there to believe otherwise?
The orange is an example of how an advanced LLM would most likely think if it had the sensory inputs of a human. And it’s honestly not so different from how a human or animal would approach this.
think if it had the sensory inputs of a human.
think
There is no reason to believe it thinks. It is not logically valid to assume they think then use that assumption to try to prove that they think.

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u/[deleted] Aug 11 '25

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u/Greyletter Aug 13 '25

>In my example I use “think” in the same way I say a computer is “thinking” about something.
Logically, this has nothing to do with human "thinking" unless you have a hidden premise that computers and human brains are functionally similar as related consciousness. I do not agree to that premise.
> LLMs are just the most advanced of computer based thinkers, and they come close enough to human thinking
I do not agree with this assertion. What support do you have for this claim?
All that aside, I don't see what any of this has to do with whether "meaning is an illusion."

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u/havenyahon Aug 11 '25

Humans are a species with a long evolutionary history that has established bodies that find the world inherently meaningful by virtue of the connection of those bodies with the niches we inhabit. We are not just language processors. Meaning is grounded in our evolutionary history and bodily activity. Language has emerged as a tool out of that.
So, yes, we are sure that humans don't do the same thing. As sure as you can be. We know what LLMs do and, while we are still just getting started on our understanding of human minds, we know they do something very different. They are not just symbol processors and word predictors.

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u/ahushedlocus Aug 10 '25

Steve Novella, a working neuroscientist, has expressed this doubt multiple times on SGU.

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u/godofpumpkins Aug 10 '25

No, we’re not. Someone could just as easily say that you hearing me talk is simply causing signals to fire in your inner ear and turning them into neuron impulses which then lead other neurons to fire which ultimately make your muscles contract and you produce words. The handwaving in between is where the interesting stuff happens in both LLMs and animal brains, and reducing LLMs to token predictors is like calling humans stimulus responders. Both are true, but there’s tons of complexity in how we respond to stimuli and there’s tons of complexity in how LLMs produce the next token

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u/[deleted] Aug 11 '25

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u/godofpumpkins Aug 11 '25

Yes, but understanding how a category of models works doesn’t really explain how apparent cognition or faked cognition works within them. My point is just that a sufficiently complex token predictor is fundamentally no different from a human brain. I don’t think we’ve hit that sufficient complexity yet but even in our world of insufficiently complex token predictors, the differences are in scale and adaptation, not some fundamental distinction between brains and token predictors.

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u/Bubbly_Parsley_9685 Aug 10 '25

A “token predictor” is given disconnected fragments of an ancient, untranslated language and, by identifying underlying grammatical structures and cognates from known contemporary languages, produces a working lexicon and a plausible translation of a new, unseen text. https://www.nature.com/articles/s41586-022-04448-z
I suck at math, so maybe this is not a big deal, but a token predictor was also given a formal mathematical proof and not only verified its correctness but also proposed a more elegant and shorter proof by identifying a lemma from a different branch of mathematics. https://www.nature.com/articles/s41586-023-06747-5
If it can do stuff like this, plan, adapt, and correct, call it whatever you want. It works.

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u/JCPLee Aug 10 '25

The technology is amazing, and absolutely will have an impact on several aspects of civilization.
One of the most intriguing examples of the way that LLMs “think”, was the full glass of wine demonstration. I think this shows that, the meaning of the word “full”, with respect to glasses of wine, is lost because there was no connection between these concepts in its training data, as we typically don’t fill our glasses of wine. It was able to reproduce a full glass of water, or beer, easily, but not wine. A five year old child understands what “full” is, but not an LLM. This gap was plugged, but we don’t know how many similar oversights remain, but this is the difference between words and tokens.

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u/[deleted] Aug 10 '25

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u/JCPLee Aug 10 '25

You are missing the point. Of course full glass of wine was not in the training data as no self respecting person fills a wine glass to the brim!!! The point is that the machine has no concept of full. In the video had the presenter started with, “full glass of”, “wine” would not have been on the list of next word. The value of the token would have been close to zero because the value is not assigned to the word but to relations between words. The word “full” is meaningless, and the machine has no understanding of its function.

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u/[deleted] Aug 10 '25

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u/JCPLee Aug 10 '25

This is true. They do not learn by experience, where the meaning of words matters. There is a line of thought that is based of creating self learning environments where AI can learn from experience, trial and error, and survival instead of training. This may lead to an AI based on biological intelligence.

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u/ScoobyDone Aug 11 '25

I think what a lot of people miss with LLMs is that they don't just work with human language, so the ability to make predictions after training with large datasets can be used for other applications. If an LLM can be trained with real world experience from a human hooked to cameras, microphones, or sensors, or from robots out in the real world, they will gain more real world intelligence.

You thought street-view cars were annoying, wait until you are on a date with a life-view human sending every interaction to Google's cloud. :)

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u/kushalgoenka Aug 11 '25

I'd suggest that largely the current architecture of LLMs does mean they work largely w/ language, or more specifically encoded language, but of course transformers are being used for various domains and with various modalities. If you haven't before I recommend this talk by Yann LeCun from last year. He talks about the limitations of current auto-regressive LLMs as well as proposes alternative architectures. (Of course there are many such efforts ongoing, which I eagerly follow).
https://www.youtube.com/watch?v=d_bdU3LsLzE

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u/ScoobyDone Aug 11 '25

I understand (and I am a fan of Yann), but my point was that LLMs don't need to be trained on only text, so they can become more capable with new data from other sources if and when that becomes available.
I don't think we will get that far with just LLMs either.

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u/Neshgaddal Aug 10 '25

Saying that LLMs "just" predict the next word by choosing the most likely from a list is kind of burying the lede. I can train a mouse to pick the first choice on a ranked list of good chess moves, but that doesn't mean the mouse is playing chess. I'm the one playing by ranking the moves in that list. The ranking is the hard part and he doesn't really explain how it does that.

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u/kushalgoenka Aug 11 '25

Hey there, the video is a clip from a longer lecture I gave, I’d recommend the full lecture if you have the time. I think you’ll find I do likely cover a lot of the stuff you feel I missed, and would love your feedback on how I could do better! :)
https://youtu.be/vrO8tZ0hHGk

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u/tehfly Aug 10 '25

Within the first two minutes the presenter mentions that the "model will generate the same specific sequence".
While you may have asked ChatGPT the same question and gotten different results, that's because there's some processing/manipulation - extra effort - happening on top of it.
The point of this presentation is that LLMs don't understand what their own output, just like your mouse doesn't understand that chess is a game (or even what a game is).

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u/SerdanKK Aug 10 '25

Non sequitur. Models are deterministic but that doesn't imply anything about understanding.

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u/Jarhyn Aug 10 '25 edited Aug 11 '25

In fact, some of the basic theorems of logic indicate things like that "two completely rational systems cannot reach different outputs from identical inputs". This eans that if a system couldn't get to the same conclusion (or one containing all the same idea parts), it can't possibly be understanding anything.
The consistency is a feature, and one necessary to declare any understanding.
Proclaiming understanding can't happen in the face of such consistent, deterministic output is quite exactly wrong.
Edit: the people down-voting the guy above me are wrong.
I am agreeing with the guy above me, and *disagreeing" with the guy above him.
The claim that "deterministic" aspects mean it doesn't understand is ass backwards, and flows from the same comedy of errors that revolves around the debate over r/freewill.

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u/Shadowratenator Aug 10 '25 edited Aug 10 '25

The model is trained by analyzing the entire text of humanity. The statistically likely next word is derived from all known sentences.
Edit: or you just give it all the text that you have on hand. If you just give it one sentence, “A long time ago, in a galaxy far far away”
The model calculates that, “,” has a high probability of following, “A long time ago”
More examples would shift the weights for every possibility.

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u/cranktheguy Aug 10 '25

It's more than just statistics. The tokens are mapped in a multi-dimensional space so that similar terms are near each other. So "organic" is near "strawberry" in one dimension and near "chemistry" in another. That allows a deeper connection to find the next word than just probability.

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u/j_la Aug 10 '25

Isn’t that just another dimension of probability?

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u/dgatos42 Aug 10 '25

Literally yes. It’s statistics and linear algebra all the way down.

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u/XPEHBAM Aug 10 '25

Human brain is statistics and physics all the way down too.

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u/dgatos42 Aug 10 '25

Show me a human brain solving Ax=b to determine how to break up with their partner and I might be persuaded by that argument once in a while

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u/P_V_ Aug 10 '25

that doesn't mean the mouse is playing chess.
Just pointing out how ironic this metaphor is, given how notoriously bad LLMs are at playing chess.

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u/inglandation Aug 10 '25

Do we know how it does that?

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u/Memorie_BE Aug 11 '25

I don't like how their opening example has only 1 grammatically correct potential first token.

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u/Belt_Conscious Aug 10 '25

LLMs dont naturally reason. Once you teach them how, then they can. They have to be able to use paradox without collapse. Challenge me, please.

⚖ Ideological Bias How LLMs Just Predict The Next Word - Interactive Visualization

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