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Fun play on words. But yes, LLMs are Large Language Models, not Large World Models. This matters because (1) the world cannot be modeled anywhere close to compl

Fun play on words. But yes, LLMs are Large Language Models, not Large World Models. This matters because (1) the world cannot be modeled anywhere close to completely with language alone, and (2) language only somewhat models the world (much in language is convention, wrong, or not concerned with modeling the world, but other concerns like persuasion, causing emotions, or fantasy / imagination). It is somewhat complicated by the fact LLMs (and VLMs) are also trained in some cases on more than simple language found on the internet (e.g. code, math, images / videos), but the same insight remains true. The interesting question is to just see how far we can get with (2) anyway.

famouswaffles 8mo ago
1. LLMs are transformers, and transformers are next state predictors. LLMs are not Language models (in the sense you are trying to imply) because even when training is restricted to only text, text is much more than language. 2. People need to let go of this strange and erroneous idea that humans somehow have this privileged access to the 'real world'. You don't. You run on a heavily filtered, tiny slice of reality. You think you understand electro-magnetism ? Tell that to the birds that innately navigate by sensing the earth's magnetic field. To them, your brain only somewhat models the real world, and evidently quite incompletely. You'll never truly understand electro-magnetism, they might say.
- tbrownaw 8mo ago
  > 2. People need to let go of this strange and erroneous idea that humans somehow have this privileged access to 'the real world'. You don't. You are denouncing a claim that the comment you're replying to did not make.
  - famouswaffles 8mo ago
    They made it implicitly, otherwise this: >(2) language only somewhat models the world is completely irrelevant. Everyone is only 'somewhat modeling' the world. Humans, Animals, and LLMs.
    - D-Machine 8mo ago
      Completely relevant, because LLMs only "somewhat model" humans' "somewhat modeling" of the world...
      - famouswaffles 8mo ago
        LLMs aren't modeling "humans modeling the world" - they're modeling patterns in data that reflect the world directly. When an LLM learns physics from textbooks, scientific papers, and code, it's learning the same compressed representations of reality that humans use, not a "model of a model." Your argument would suggest that because you learned about quantum mechanics through language (textbooks, lectures), you only have access to "humans' modeling of humans' modeling of quantum mechanics" - an infinite regress that's clearly absurd.
        D-Machine 8mo ago
        > LLMs aren't modeling "humans modeling the world" - they're modeling patterns in data that reflect the world directly. This is a deranged and factually and tautologically (definitionally) false claim. LLMs can only work with tokenizations of texts written by people who produce those text to represent their actual models. All this removal and all these intermediate representational steps make LLMs a priori obviously even more distant from reality than humans. This is all definitional, what you are saying is just nonsense. > When an LLM learns physics from textbooks, scientific papers, and code, it's learning the same compressed representations of reality that humans use, not a "model of a model." A model is a compressed representation of reality. Physics is a model of the mechanics of various parts of the universe, i.e. "learning physics" is "learning a physical model". So, clarifying, the above sentence is > When an LLM learns physical models from textbooks, scientific papers, and code, it's learning the model of reality that humans use, not a "model of a model." This is clearly factually wrong, as the model that humans actually use is not the summaries written in textbooks, but the actual embodied and symbolic model that they use in reality, and which they only translate in corrupted and simplified, limited form to text (and that latter diminished form of all things is all the LLM can see). It is also not clear the LLM learns to actually do physics: it only learns how to write about physics like how humans do, but it doesn't mean it can run labs, interpret experiments, or apply models to novel contexts like humans can, or operate at the same level as humans. It clearly is learning something different from humans because it doesn't have the same sources of info. > Your argument would suggest that because you learned about quantum mechanics through language (textbooks, lectures), you only have access to "humans' modeling of humans' modeling of quantum mechanics" - an infinite regress that's clearly absurd. There is no infinite regress: humans actually verify that the things they learn and say are correct and provide effects, and update models accordingly. They do this by trying behaviours consistent with the learned model, and seeing how reality (other people, the physical world) responds (in degree and kind). LLMs have no conception of correctness or truth (not in any of the loss functions), and are trained and then done. Humans can't learn solely from digesting texts either. Anyone who has done math knows that reading a textbook doesn't teach you almost anything, you have to actually solve the problems (and attempted-solving is not in much/any texts) and discuss your solutions and reasoning with others. Other domains involving embodied skills, like cooking, require other kinds of feedback from the environment and others. But LLMs are imprisoned in tokens. EDIT: No serious researcher thinks LLMs are the way to AGI, this hasn't been a controversial opinion even among enthusiasts since about mid-2025 or so. This stuff about language is all trivial and basic stuff accepted by people in the field, and why things like V-JEPA-2 are being researched. So the comments here attempting to argue otherwise are really quite embarrassing.
- D-Machine 8mo ago
  LLMs are language models, something being a transformer or next-state predictor does not make it a language model. You can also have e.g. convolutional language models or LSTM-based language models. This is a basic point that anyone with any proper understanding of these models would know. Even if you disagree with these semantics, the major LLMs today are primarily trained on natural language. But, yes, as I said in another comment on this thread, it isn't that simple, because LLMs today are trained on tokens from tokenizers, and these tokenizers are trained on text that includes e.g. natural language, mathematical symbolism, and code. Yes, humans have incredibly limited access to the real world. But they experience and model this world with far more tools and machinery than language. Sometimes, in certain cases, they attempt to messily translate this messy, multimodal understanding into tokens, and then make those tokens available on the internet. An LLM (in the sense everyone means it, which, again, is largely a natural language model, but certainly just a tokenized text model) has access only to these messy tokens, so, yes, far less capacity than humanity collectively. And though the LLM can integrate knowledge from a massive amount of tokens from a huge amount of humans, even a single human has more different kinds of sensory information and modality-specific knowledge than the LLM. So humans DO have more privileged access to the real world than LLMs (even though we can barely access a slice of reality at all).
  - famouswaffles 8mo ago
    >LLMs are language models, something being a transformer or next-state predictors does not make it a language model. You can also have e.g. convolutional language models or LSTM-based language models. This is a basic point that anyone with any proper understanding of these models would know. 'Language Model' has no inherent meaning beyond 'predicts natural language sequences'. You are trying to make it mean more than that. You can certainly make something you'd call a language model with convolution or LSTMs, but that's just a semantics game. In practice, they would not work like transformers and would in fact perform much worse than them with the same compute budget. >Even if you disagree with these semantics, the major LLMs today are primarily trained on natural language. The major LLMs today are trained on trillions of tokens of text, much of which has nothing to do with language beyond the means of communication, millions of images and million(s) of hours of audio. The problem as I tried to explain is that you're packing more meaning into 'Language Model' than you should. Being trained on text does not mean all your responses are modelled via language as you seem to imply. Even for a model trained on text, only the first and last few layers of a LLM concerns language.
- rockinghigh 8mo ago
  A language model in computer science is a model that predicts the probability of a sentence or a word given a sentence. This definition predates LLMs.
  - famouswaffles 8mo ago
    A 'language model' only has meaning in so far as it tells you this thing 'predicts natural language sequences'. It does not tell you how these sequences are being predicted or any anything about what's going on inside, so all the extra meaning OP is trying to place by calling them Language Models is well...misplaced. That's the point I was trying to make.
- phailhaus 8mo ago
  > People need to let go of this strange and erroneous idea that humans somehow have this privileged access to the 'real world'. This is irrelevant, the point is that you do have access to a world which LLMs don't, at all. They only get the text we produce after we interact with the world. It is working with "compressed data" at all times, and have absolutely no idea what we subconsciously internalized that we decided not to write down or why.
  - famouswaffles 8mo ago
    All of the SOTA LLMs today are trained on more than text. It doesn't matter whether LLMs have "complete" (nothing does) or human-like world access, but whether the compression in text is lossy in ways that fundamentally prevent useful world modeling or reconstruction. And empirically... it doesn't seem to be. Text contains an enormous amount of implicit structure about how the world works, precisely because humans writing it did interact with the world and encoded those patterns. And your subconscious is far leakier than you imagine. Your internal state will bleed into your writing, one way or another whether you're aware of it or not. Models can learn to reconstruct arithmetic algorithms given just operation and answer with no instruction. What sort of things have LLMs reconstructed after being trained on trillions of tokens of data ?
throw310822 8mo ago
Large Language Models is a misnomer- these things were originally trained to reproduce language, but they went far beyond that. The fact that they're trained on language (if that's even still the case) is irrelevant- it's like claiming that student trained on quizzes and exercise books are only able to solve quizzes and exercises.
- D-Machine 8mo ago
  It isn't a misnomer at all, and comments like yours are why it is increasingly important to remind people about the linguistic foundations of these models. For example, no matter many books you read about riding a bike, you still need to actually get on a bike and do some practice before you can ride it. The reading can certainly help, at least in theory, but, in practice, is not necessary and may even hurt (if it makes certain processes that need to be unconscious held too strongly in consciousness, due to the linguistic model presented in the book). This is why LLMs being so strongly tied to natural language is still an important limitation (even it is clearly less limiting than most expected).
  - CamperBob2 8mo ago
    You and I can't learn to ride a bike by reading thousands of books about cycling and Newtonian physics, but a robot driven by an LLM-like process certainly can. In practice it would make heavy use of RL, as humans do.
    - wrs 8mo ago
      I have no idea why you used the word “certainly” there.
      - CamperBob2 8mo ago
        What is in the nature of bike-riding that cannot be reduced to text? You know transformers can do math, right?
thomasahle 8mo ago
> This matters because (1) the world cannot be modeled anywhere close to completely with language alone LLMs being "Language Models" means they model language, it doesn't mean they "model the world with language". On the contrary, modeling language requires you to also model the world, but that's in the hidden state, and not using language.
- D-Machine 8mo ago
  Let's be more precise: LLMs have to model the world from an intermediate tokenized representation of the text on the internet. Most of this text is natural language, but to allow for e.g. code and math, let's say "tokens" to keep it generic, even though in practice, tokens mostly tokenize natural language. LLMs can only model tokens, and tokens are produced by humans trying to model the world. Tokenized models are NOT the only kinds of models humans can produce (we can have visual, kinaesthetic, tactile, gustatory, and all sorts of sensory, non-linguistic models of the world). LLMs are trained on tokenizations of text, and most of that text is humans attempting to translate their various models of the world into tokenized form. I.e. humans make tokenized models of their actual models (which are still just messy models of the world), and this is what LLMs are trained on. So, do "LLMS model the world with language"? Well, they are constrained in that they can only model the world that is already modeled by language (generally: tokenized). So the "with" here is vague. But patterns encoded in the hidden state are still patterns of tokens. Humans can have models that are much more complicated than patterns of tokens. Non-LLM models (e.g. models connected to sensors, such as those in self-driving vehicles, and VLMs) can use more than simple linguistic tokens to model the world, but LLMs are deeply constrained relative to humans, in this very specific sense.
  - lithocarpus 8mo ago
    I don't get the importance of the distinction really. Don't LLMs and Large non-language Models fundamentally work kind of similarly underneath? And use similar kinds of hardware? But I know very little about this.
    - aixpert 8mo ago
energy123 8mo ago
Modern LLMs are large token models. I believe you can model the world at a sufficient granularity with token sequences. You can pack a lot of information into a sequence of 1 million tokens.
- andsoitis 8mo ago
  > I believe you can model the world at a sufficient granularity with token sequences. Sufficient for what?
  - weregiraffe 8mo ago
    Sufficient to fool a lot of investors, at least.
- weregiraffe 8mo ago
  Let's be accurate. LLMs are large text-corpus models. The texts are encoded as tokens, but that's just implementation detail.

famouswaffles 8mo ago

1. LLMs are transformers, and transformers are next state predictors. LLMs are not Language models (in the sense you are trying to imply) because even when training is restricted to only text, text is much more than language. 2. People need to let go of this strange and erroneous idea that humans somehow have this privileged access to the 'real world'. You don't. You run on a heavily filtered, tiny slice of reality. You think you understand electro-magnetism ? Tell that to the birds that innately navigate by sensing the earth's magnetic field. To them, your brain only somewhat models the real world, and evidently quite incompletely. You'll never truly understand electro-magnetism, they might say.

tbrownaw 8mo ago
> 2. People need to let go of this strange and erroneous idea that humans somehow have this privileged access to 'the real world'. You don't. You are denouncing a claim that the comment you're replying to did not make.
- famouswaffles 8mo ago
  They made it implicitly, otherwise this: >(2) language only somewhat models the world is completely irrelevant. Everyone is only 'somewhat modeling' the world. Humans, Animals, and LLMs.
  - D-Machine 8mo ago
    Completely relevant, because LLMs only "somewhat model" humans' "somewhat modeling" of the world...
    - famouswaffles 8mo ago
      LLMs aren't modeling "humans modeling the world" - they're modeling patterns in data that reflect the world directly. When an LLM learns physics from textbooks, scientific papers, and code, it's learning the same compressed representations of reality that humans use, not a "model of a model." Your argument would suggest that because you learned about quantum mechanics through language (textbooks, lectures), you only have access to "humans' modeling of humans' modeling of quantum mechanics" - an infinite regress that's clearly absurd.
      - D-Machine 8mo ago
        > LLMs aren't modeling "humans modeling the world" - they're modeling patterns in data that reflect the world directly. This is a deranged and factually and tautologically (definitionally) false claim. LLMs can only work with tokenizations of texts written by people who produce those text to represent their actual models. All this removal and all these intermediate representational steps make LLMs a priori obviously even more distant from reality than humans. This is all definitional, what you are saying is just nonsense. > When an LLM learns physics from textbooks, scientific papers, and code, it's learning the same compressed representations of reality that humans use, not a "model of a model." A model is a compressed representation of reality. Physics is a model of the mechanics of various parts of the universe, i.e. "learning physics" is "learning a physical model". So, clarifying, the above sentence is > When an LLM learns physical models from textbooks, scientific papers, and code, it's learning the model of reality that humans use, not a "model of a model." This is clearly factually wrong, as the model that humans actually use is not the summaries written in textbooks, but the actual embodied and symbolic model that they use in reality, and which they only translate in corrupted and simplified, limited form to text (and that latter diminished form of all things is all the LLM can see). It is also not clear the LLM learns to actually do physics: it only learns how to write about physics like how humans do, but it doesn't mean it can run labs, interpret experiments, or apply models to novel contexts like humans can, or operate at the same level as humans. It clearly is learning something different from humans because it doesn't have the same sources of info. > Your argument would suggest that because you learned about quantum mechanics through language (textbooks, lectures), you only have access to "humans' modeling of humans' modeling of quantum mechanics" - an infinite regress that's clearly absurd. There is no infinite regress: humans actually verify that the things they learn and say are correct and provide effects, and update models accordingly. They do this by trying behaviours consistent with the learned model, and seeing how reality (other people, the physical world) responds (in degree and kind). LLMs have no conception of correctness or truth (not in any of the loss functions), and are trained and then done. Humans can't learn solely from digesting texts either. Anyone who has done math knows that reading a textbook doesn't teach you almost anything, you have to actually solve the problems (and attempted-solving is not in much/any texts) and discuss your solutions and reasoning with others. Other domains involving embodied skills, like cooking, require other kinds of feedback from the environment and others. But LLMs are imprisoned in tokens. EDIT: No serious researcher thinks LLMs are the way to AGI, this hasn't been a controversial opinion even among enthusiasts since about mid-2025 or so. This stuff about language is all trivial and basic stuff accepted by people in the field, and why things like V-JEPA-2 are being researched. So the comments here attempting to argue otherwise are really quite embarrassing.
D-Machine 8mo ago
LLMs are language models, something being a transformer or next-state predictor does not make it a language model. You can also have e.g. convolutional language models or LSTM-based language models. This is a basic point that anyone with any proper understanding of these models would know. Even if you disagree with these semantics, the major LLMs today are primarily trained on natural language. But, yes, as I said in another comment on this thread, it isn't that simple, because LLMs today are trained on tokens from tokenizers, and these tokenizers are trained on text that includes e.g. natural language, mathematical symbolism, and code. Yes, humans have incredibly limited access to the real world. But they experience and model this world with far more tools and machinery than language. Sometimes, in certain cases, they attempt to messily translate this messy, multimodal understanding into tokens, and then make those tokens available on the internet. An LLM (in the sense everyone means it, which, again, is largely a natural language model, but certainly just a tokenized text model) has access only to these messy tokens, so, yes, far less capacity than humanity collectively. And though the LLM can integrate knowledge from a massive amount of tokens from a huge amount of humans, even a single human has more different kinds of sensory information and modality-specific knowledge than the LLM. So humans DO have more privileged access to the real world than LLMs (even though we can barely access a slice of reality at all).
- famouswaffles 8mo ago
  >LLMs are language models, something being a transformer or next-state predictors does not make it a language model. You can also have e.g. convolutional language models or LSTM-based language models. This is a basic point that anyone with any proper understanding of these models would know. 'Language Model' has no inherent meaning beyond 'predicts natural language sequences'. You are trying to make it mean more than that. You can certainly make something you'd call a language model with convolution or LSTMs, but that's just a semantics game. In practice, they would not work like transformers and would in fact perform much worse than them with the same compute budget. >Even if you disagree with these semantics, the major LLMs today are primarily trained on natural language. The major LLMs today are trained on trillions of tokens of text, much of which has nothing to do with language beyond the means of communication, millions of images and million(s) of hours of audio. The problem as I tried to explain is that you're packing more meaning into 'Language Model' than you should. Being trained on text does not mean all your responses are modelled via language as you seem to imply. Even for a model trained on text, only the first and last few layers of a LLM concerns language.
rockinghigh 8mo ago
A language model in computer science is a model that predicts the probability of a sentence or a word given a sentence. This definition predates LLMs.
- famouswaffles 8mo ago
  A 'language model' only has meaning in so far as it tells you this thing 'predicts natural language sequences'. It does not tell you how these sequences are being predicted or any anything about what's going on inside, so all the extra meaning OP is trying to place by calling them Language Models is well...misplaced. That's the point I was trying to make.
phailhaus 8mo ago
> People need to let go of this strange and erroneous idea that humans somehow have this privileged access to the 'real world'. This is irrelevant, the point is that you do have access to a world which LLMs don't, at all. They only get the text we produce after we interact with the world. It is working with "compressed data" at all times, and have absolutely no idea what we subconsciously internalized that we decided not to write down or why.
- famouswaffles 8mo ago
  All of the SOTA LLMs today are trained on more than text. It doesn't matter whether LLMs have "complete" (nothing does) or human-like world access, but whether the compression in text is lossy in ways that fundamentally prevent useful world modeling or reconstruction. And empirically... it doesn't seem to be. Text contains an enormous amount of implicit structure about how the world works, precisely because humans writing it did interact with the world and encoded those patterns. And your subconscious is far leakier than you imagine. Your internal state will bleed into your writing, one way or another whether you're aware of it or not. Models can learn to reconstruct arithmetic algorithms given just operation and answer with no instruction. What sort of things have LLMs reconstructed after being trained on trillions of tokens of data ?

throw310822 8mo ago

Large Language Models is a misnomer- these things were originally trained to reproduce language, but they went far beyond that. The fact that they're trained on language (if that's even still the case) is irrelevant- it's like claiming that student trained on quizzes and exercise books are only able to solve quizzes and exercises.

D-Machine 8mo ago
It isn't a misnomer at all, and comments like yours are why it is increasingly important to remind people about the linguistic foundations of these models. For example, no matter many books you read about riding a bike, you still need to actually get on a bike and do some practice before you can ride it. The reading can certainly help, at least in theory, but, in practice, is not necessary and may even hurt (if it makes certain processes that need to be unconscious held too strongly in consciousness, due to the linguistic model presented in the book). This is why LLMs being so strongly tied to natural language is still an important limitation (even it is clearly less limiting than most expected).
- CamperBob2 8mo ago
  You and I can't learn to ride a bike by reading thousands of books about cycling and Newtonian physics, but a robot driven by an LLM-like process certainly can. In practice it would make heavy use of RL, as humans do.
  - wrs 8mo ago
    I have no idea why you used the word “certainly” there.
    - CamperBob2 8mo ago
      What is in the nature of bike-riding that cannot be reduced to text? You know transformers can do math, right?

thomasahle 8mo ago

> This matters because (1) the world cannot be modeled anywhere close to completely with language alone LLMs being "Language Models" means they model language, it doesn't mean they "model the world with language". On the contrary, modeling language requires you to also model the world, but that's in the hidden state, and not using language.

D-Machine 8mo ago
Let's be more precise: LLMs have to model the world from an intermediate tokenized representation of the text on the internet. Most of this text is natural language, but to allow for e.g. code and math, let's say "tokens" to keep it generic, even though in practice, tokens mostly tokenize natural language. LLMs can only model tokens, and tokens are produced by humans trying to model the world. Tokenized models are NOT the only kinds of models humans can produce (we can have visual, kinaesthetic, tactile, gustatory, and all sorts of sensory, non-linguistic models of the world). LLMs are trained on tokenizations of text, and most of that text is humans attempting to translate their various models of the world into tokenized form. I.e. humans make tokenized models of their actual models (which are still just messy models of the world), and this is what LLMs are trained on. So, do "LLMS model the world with language"? Well, they are constrained in that they can only model the world that is already modeled by language (generally: tokenized). So the "with" here is vague. But patterns encoded in the hidden state are still patterns of tokens. Humans can have models that are much more complicated than patterns of tokens. Non-LLM models (e.g. models connected to sensors, such as those in self-driving vehicles, and VLMs) can use more than simple linguistic tokens to model the world, but LLMs are deeply constrained relative to humans, in this very specific sense.
- lithocarpus 8mo ago
  I don't get the importance of the distinction really. Don't LLMs and Large non-language Models fundamentally work kind of similarly underneath? And use similar kinds of hardware? But I know very little about this.
  - aixpert 8mo ago

energy123 8mo ago

Modern LLMs are large token models. I believe you can model the world at a sufficient granularity with token sequences. You can pack a lot of information into a sequence of 1 million tokens.

andsoitis 8mo ago
> I believe you can model the world at a sufficient granularity with token sequences. Sufficient for what?
- weregiraffe 8mo ago
  Sufficient to fool a lot of investors, at least.
weregiraffe 8mo ago
Let's be accurate. LLMs are large text-corpus models. The texts are encoded as tokens, but that's just implementation detail.