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We already know LLMs are more than just words, there are literally papers demonstrating the world models they build. One of the problems is that LLMs build thos

We already know LLMs are more than just words, there are literally papers demonstrating the world models they build. One of the problems is that LLMs build those world models from impoverished sensory apparatus (the digital word token), so the relations they build between the concepts behind words are weaker than humans who build deeper multimodal relations over a lifetime. Multimodal LLMs have been shown to significantly outperform classic LLMs of comparable size, and that's still a weak dataset compared to human training.

dartos 2y ago
> We already know LLMs are more than just words, Just because you say something doesn’t mean it’s true. They are literally next token prediction machines normally trained on just text tokens. All they know is words. It happens that we humans encode and assign a lot of meaning in words and their semantics. LLMs can replicate combinations of words that appear to have this intent and understanding, even though they literally can’t, as they were just statistically likely next tokens. (Not that knowing likely next tokens isn’t useful, but it’s far from understanding) Any assignment of meaning, reasoning, or whatever that we humans assign is personification bias. Machines designed to spit out convincing text successfully spits out convincing text and now swaths of people think that more is going on. I’m not as well versed on multimodal models, but the ideas should be consistent. They are guessing statistically likely next tokens, regardless of if those tokens represent text or audio or images or whatever. Not useless at all, but not this big existential advancement some people seem to think it is. The whole AGI hype is very similar to “theory of everything” hype that comes and goes now and again.
- naasking 2y ago
  > They are literally next token prediction machines normally trained on just text tokens. And in order to predict the next token well they have to build world models, otherwise they would just output nonsense. This has been proven [1]. This notion that just calling them "next token predictors" somehow precludes them being intelligent is based on a premise that human intelligence cannot be reduced to next token prediction, but nobody has proven any such thing! In fact, our best models for human cognition are literally predictive coding. LLMs are probably not the final story in AGI, but claiming they are not reasoning or not understanding is at best speculation, because we lack a mechanistic understanding of what "understanding" and "reasoning" actually mean. In other words, you don't know that you are not just a fancy next token predictor. [1] https://arxiv.org/abs/2310.02207 https://arxiv.org/abs/2310.02207
  - krainboltgreene 2y ago
    > based on a premise that human intelligence cannot be reduced to next token prediction It can't. No one with any credentials in the study of human intelligence is saying that unless they're talking to like high schoolers as a way of simplifying a complex field.
    - naasking 2y ago
      This is either bullshit or tautologically true, depending specifically what you mean. The study of human intelligence does not take place at the level of tokens, so of course they wouldn't say that. The whole field is arguably reducible to physical phenomena though, and fundamental physical beables are devoid of intrinsic semantic content, and thus can be ultimately represented by tokens. What ultimately matters is the constructed high dimensional network that relates tokens and the algorithm that can traverse, encode and decode this network, that's what encodes knowledge.
      - krainboltgreene 2y ago
        No. You're wrong about this. You cannot simply reduce human intelligence to this definition and also be correct.
        naasking 2y ago
        Because...?
        krainboltgreene 2y ago
        For the same reason you can't reduce a human to simply a bag of atoms and expect to understand the person.
        naasking 2y ago
        But humans are a specific type of a bag of atoms, and humans do (mostly) understand what they say and do, so that's not a legitimate argument against the reducibility of "understanding" to a such a bag of atoms (or specific kind of next token prediction for LLMs).
- nuancebydefault 2y ago
  > Just because you say something doesn’t mean it’s true. They are literally next token prediction machines normally trained on just text tokens. Just because you say something doesn’t mean it’s true.

dartos 2y ago

> We already know LLMs are more than just words, Just because you say something doesn’t mean it’s true. They are literally next token prediction machines normally trained on just text tokens. All they know is words. It happens that we humans encode and assign a lot of meaning in words and their semantics. LLMs can replicate combinations of words that appear to have this intent and understanding, even though they literally can’t, as they were just statistically likely next tokens. (Not that knowing likely next tokens isn’t useful, but it’s far from understanding) Any assignment of meaning, reasoning, or whatever that we humans assign is personification bias. Machines designed to spit out convincing text successfully spits out convincing text and now swaths of people think that more is going on. I’m not as well versed on multimodal models, but the ideas should be consistent. They are guessing statistically likely next tokens, regardless of if those tokens represent text or audio or images or whatever. Not useless at all, but not this big existential advancement some people seem to think it is. The whole AGI hype is very similar to “theory of everything” hype that comes and goes now and again.

naasking 2y ago
> They are literally next token prediction machines normally trained on just text tokens. And in order to predict the next token well they have to build world models, otherwise they would just output nonsense. This has been proven [1]. This notion that just calling them "next token predictors" somehow precludes them being intelligent is based on a premise that human intelligence cannot be reduced to next token prediction, but nobody has proven any such thing! In fact, our best models for human cognition are literally predictive coding. LLMs are probably not the final story in AGI, but claiming they are not reasoning or not understanding is at best speculation, because we lack a mechanistic understanding of what "understanding" and "reasoning" actually mean. In other words, you don't know that you are not just a fancy next token predictor. [1] https://arxiv.org/abs/2310.02207 https://arxiv.org/abs/2310.02207
- krainboltgreene 2y ago
  > based on a premise that human intelligence cannot be reduced to next token prediction It can't. No one with any credentials in the study of human intelligence is saying that unless they're talking to like high schoolers as a way of simplifying a complex field.
  - naasking 2y ago
    This is either bullshit or tautologically true, depending specifically what you mean. The study of human intelligence does not take place at the level of tokens, so of course they wouldn't say that. The whole field is arguably reducible to physical phenomena though, and fundamental physical beables are devoid of intrinsic semantic content, and thus can be ultimately represented by tokens. What ultimately matters is the constructed high dimensional network that relates tokens and the algorithm that can traverse, encode and decode this network, that's what encodes knowledge.
    - krainboltgreene 2y ago
      No. You're wrong about this. You cannot simply reduce human intelligence to this definition and also be correct.
      - naasking 2y ago
        Because...?
        krainboltgreene 2y ago
        For the same reason you can't reduce a human to simply a bag of atoms and expect to understand the person.
        naasking 2y ago
        But humans are a specific type of a bag of atoms, and humans do (mostly) understand what they say and do, so that's not a legitimate argument against the reducibility of "understanding" to a such a bag of atoms (or specific kind of next token prediction for LLMs).
nuancebydefault 2y ago
> Just because you say something doesn’t mean it’s true. They are literally next token prediction machines normally trained on just text tokens. Just because you say something doesn’t mean it’s true.