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Transformer produce the next token by manipulating K hidden vectors per layer, one vector per preceding token. So yes you can increase compute length arbitraril

Transformer produce the next token by manipulating K hidden vectors per layer, one vector per preceding token. So yes you can increase compute length arbitrarily by increasing tokens. Those tokens don't have to carry any information to work. https://arxiv.org/abs/2310.02226 https://arxiv.org/abs/2310.02226 And again, human brains are clearly limited in the number of steps it can compute without writing something down. Limited =/ Trivial >FYI, "attention is all you need" has the implicit context of "if all you want to build is a language model". Great. Do you know what a "language model" is capable of in the limit ? No These top research labs aren't only working on Transformers as they currently exist but it doesn't make much sense to abandon a golden goose before it has hit a wall.

HarHarVeryFunny 2y ago
You are confusing number of sequential steps with total amount of compute spent. The input sequence is processed in parallel, regardless of length, so number of tokens has no impact on number of sequential compute steps which is always N=layers. > Do you know what a "language model" is capable of in the limit ? Well, yeah, if the language model is an N-layer transformer ...
- famouswaffles 2y ago
  Fair Enough. Then increase N (N is almost always increased when a model is scaled up) and train or write things down and continue. A limitless iteration machine (without external aid) is currently an idea of fiction. Brains can't do it so I'm not particularly worried if machines can't either.
  - HarHarVeryFunny 2y ago
    Increasing number of layers isn't a smart way to solve it. It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". This lack of "variable compute" is a widely recognized shortcoming of transformer-based LLMs, and there are plenty of others. The point apropos this thread is that you can't just train an LLM to be something that it is not. If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process... the best it can do is to model the outcome of the generative process, not the process itself. The difference is that without having learnt the generative process, the model will fail when presented with a novel input that it didn't see during training, and therefore didn't memorize the "cheat sheet" answer for.
    - famouswaffles 2y ago
      >Increasing number of layers isn't a smart way to solve it. The "smart way" is a luxury. Solving the problem is what matters. Think of a smart way later if you can. That's how a lot of technological advancement has worked. >It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". Same thing. Efficiency is nice but a secondary concern. >If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process. Every inference problem has itself a fixed number of compute steps it needs (yes even your chess move). Variability is a nice thing for between inferences(maybe move 1 required 500 but 2 only 240 etc) A nice thing but never a necessary thing. 3.5-turbo-instruct plays chess consistently at 1800 Elo so clearly the N of the current SOTA is already enough to play non-trivial chess at a level beyond most humans. There is an N large enough for every GI problem humans care about. Not to sound like a broken record but once again, limited =/ trivial.
HarHarVeryFunny 2y ago
> And again, human brains are clearly limited in the number of steps it can compute without writing something down No - there is a loop between the cortex and thalamus, feeding the outputs of the cortex back in as inputs. Our brain can iterate for as long as it likes before initiating any motor output, if any, such as writing something down.
- famouswaffles 2y ago
  The brain's ability to iterate on information is still constrained by certain cognitive limitations like working memory capacity and attention span. In practice, the cortex-thalamus loop allows for some degree of internal iteration, but the brain cannot endlessly iterate without some form of external aid (e.g., writing something down) to offload information and prevent cognitive overload. I'm not telling you anything here you don't experience in your everyday life. Try indefinitely iterating on any computation you like and see how well that works for you.
  - HarHarVeryFunny 2y ago
    What's your point? The discussion is about the architecturally imposed limitations of LLMs, resulting in capabilities that are way less than that of a brain. The fact that the brain has it's own limits doesn't somehow negate this fact!
    - famouswaffles 2y ago
      My point is that for some bizare reason, people have standards of reasoning (for machines) that only exist in fiction or their own imagination. It is beyond silly to dump an architecture for a limitation the human brain has. A reasoning engine that can iterate indefinitely with no external aid does not exist in real life. That the transformer also has this weakness is not any reason for it to have capabilities less than a brain so it's completely moot.
      - HarHarVeryFunny 2y ago

HarHarVeryFunny 2y ago

You are confusing number of sequential steps with total amount of compute spent. The input sequence is processed in parallel, regardless of length, so number of tokens has no impact on number of sequential compute steps which is always N=layers. > Do you know what a "language model" is capable of in the limit ? Well, yeah, if the language model is an N-layer transformer ...

famouswaffles 2y ago
Fair Enough. Then increase N (N is almost always increased when a model is scaled up) and train or write things down and continue. A limitless iteration machine (without external aid) is currently an idea of fiction. Brains can't do it so I'm not particularly worried if machines can't either.
- HarHarVeryFunny 2y ago
  Increasing number of layers isn't a smart way to solve it. It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". This lack of "variable compute" is a widely recognized shortcoming of transformer-based LLMs, and there are plenty of others. The point apropos this thread is that you can't just train an LLM to be something that it is not. If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process... the best it can do is to model the outcome of the generative process, not the process itself. The difference is that without having learnt the generative process, the model will fail when presented with a novel input that it didn't see during training, and therefore didn't memorize the "cheat sheet" answer for.
  - famouswaffles 2y ago
    >Increasing number of layers isn't a smart way to solve it. The "smart way" is a luxury. Solving the problem is what matters. Think of a smart way later if you can. That's how a lot of technological advancement has worked. >It order to be able to reason effectively and efficiently the model needs to use as much, or as little, compute as needed for a given task. Completing "1+1=" should take less compute steps than "A winning sequence for white here is ...". Same thing. Efficiency is nice but a secondary concern. >If the generating process required variable compute (maybe 1000's of steps) - e.g. to come up with a chess move - then no amount of training can make the LLM converge to model this generative process. Every inference problem has itself a fixed number of compute steps it needs (yes even your chess move). Variability is a nice thing for between inferences(maybe move 1 required 500 but 2 only 240 etc) A nice thing but never a necessary thing. 3.5-turbo-instruct plays chess consistently at 1800 Elo so clearly the N of the current SOTA is already enough to play non-trivial chess at a level beyond most humans. There is an N large enough for every GI problem humans care about. Not to sound like a broken record but once again, limited =/ trivial.

HarHarVeryFunny 2y ago

> And again, human brains are clearly limited in the number of steps it can compute without writing something down No - there is a loop between the cortex and thalamus, feeding the outputs of the cortex back in as inputs. Our brain can iterate for as long as it likes before initiating any motor output, if any, such as writing something down.

famouswaffles 2y ago
The brain's ability to iterate on information is still constrained by certain cognitive limitations like working memory capacity and attention span. In practice, the cortex-thalamus loop allows for some degree of internal iteration, but the brain cannot endlessly iterate without some form of external aid (e.g., writing something down) to offload information and prevent cognitive overload. I'm not telling you anything here you don't experience in your everyday life. Try indefinitely iterating on any computation you like and see how well that works for you.
- HarHarVeryFunny 2y ago
  What's your point? The discussion is about the architecturally imposed limitations of LLMs, resulting in capabilities that are way less than that of a brain. The fact that the brain has it's own limits doesn't somehow negate this fact!
  - famouswaffles 2y ago
    My point is that for some bizare reason, people have standards of reasoning (for machines) that only exist in fiction or their own imagination. It is beyond silly to dump an architecture for a limitation the human brain has. A reasoning engine that can iterate indefinitely with no external aid does not exist in real life. That the transformer also has this weakness is not any reason for it to have capabilities less than a brain so it's completely moot.
    - HarHarVeryFunny 2y ago