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> Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the re

> Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place. there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.

deleted 2y ago
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famouswaffles 2y ago
> there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers. fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fundamentally cannot be accurate on that many digits. https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2307.03381 https://arxiv.org/abs/2307.03381 https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989 Ultimately, numbers are represented in GPT like systems in a pretty weird way. That way affects how well they can learn to do things like arithmetic and counting and the like but that way isn't a necessary way. You don't have to represent numbers like that.
- riku_iki 2y ago
  > https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2405.17399 they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and reproduce algorithm. They also demonstrated that transformer can't learn sorting.
  - famouswaffles 2y ago
    They did not build a specialized model. They changed the embeddings. You can do this for any existing model. >They also demonstrated that transformer can't learn sorting. They did not demonstrate anything of the sort.
    - riku_iki 2y ago
      What you are saying contradicts to my reading of publication.
      - famouswaffles 2y ago
        Their method does not require building specialized models from scratch (you can but you don't have to) and they did not prove transformers can't learn sorting. If you think they did, then you don't understand what it means to prove something.
        riku_iki 2y ago
        In my books what they built (specialized training data + specialized embeddings format) is exactly specialized model. You can disagree of course and say again that I don't understand something, but discussion will be over.
        famouswaffles 2y ago
        The data isn't specialized ? A poor result when testing one model is not proof that the architecture behind the model is incapable of getting good results. It's just that simple. The same way seeing the OG GPT-3 fail at chess was not proof LLMs can't play chess. This >They also demonstrated that transformer can't learn sorting. is just wrong. Nothing more to it.
  - famouswaffles 2y ago
    >I also think most of the accuracy came from memorization of training set Oh yes..it memorized a 20 digit training set to solve 100 digit problems. That makes sense. Lol >(model didn't provide intermediate results, and started failing significantly at sligtly larger input). No it didn't. They tested up to 100 digits with very high accuracy. I don't think you even read the abstract of this, nevermind the actual paper.

deleted 2y ago

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famouswaffles 2y ago

> there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers. fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fundamentally cannot be accurate on that many digits. https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2307.03381 https://arxiv.org/abs/2307.03381 https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989 Ultimately, numbers are represented in GPT like systems in a pretty weird way. That way affects how well they can learn to do things like arithmetic and counting and the like but that way isn't a necessary way. You don't have to represent numbers like that.

riku_iki 2y ago
> https://arxiv.org/abs/2405.17399 https://arxiv.org/abs/2405.17399 they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and reproduce algorithm. They also demonstrated that transformer can't learn sorting.
- famouswaffles 2y ago
  They did not build a specialized model. They changed the embeddings. You can do this for any existing model. >They also demonstrated that transformer can't learn sorting. They did not demonstrate anything of the sort.
  - riku_iki 2y ago
    What you are saying contradicts to my reading of publication.
    - famouswaffles 2y ago
      Their method does not require building specialized models from scratch (you can but you don't have to) and they did not prove transformers can't learn sorting. If you think they did, then you don't understand what it means to prove something.
      - riku_iki 2y ago
        In my books what they built (specialized training data + specialized embeddings format) is exactly specialized model. You can disagree of course and say again that I don't understand something, but discussion will be over.
        famouswaffles 2y ago
        The data isn't specialized ? A poor result when testing one model is not proof that the architecture behind the model is incapable of getting good results. It's just that simple. The same way seeing the OG GPT-3 fail at chess was not proof LLMs can't play chess. This >They also demonstrated that transformer can't learn sorting. is just wrong. Nothing more to it.
- famouswaffles 2y ago
  >I also think most of the accuracy came from memorization of training set Oh yes..it memorized a 20 digit training set to solve 100 digit problems. That makes sense. Lol >(model didn't provide intermediate results, and started failing significantly at sligtly larger input). No it didn't. They tested up to 100 digits with very high accuracy. I don't think you even read the abstract of this, nevermind the actual paper.