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>How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform th

>How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform this task"? 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. People just have their pre-conceptions about how they think LLMs should work and what their "very nature" should preclude and are so very confident about it. People like that will say things like "LLM are always hallucinating. It doesn't know the difference between truth and fiction!" and feel like they've just said something profound about the "nature" of LLMs, all while being entirely wrong (no need to wait, plenty of different research to trash this particular take). It's just very funny seeing people who were/would be gob smacked a few years ago talking about the "very nature" of LLMs. If you understood this nature so well, why didn't you all tell us about what it would be able to do years ago ? ML is an alchemical science. The builders themselves don't understand the "very nature" of anything they're building, nevermind anyone else.

riku_iki 2y ago
> 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.
        2y ago

riku_iki 2y ago

> 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
[deleted]
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.
        2y ago