datsci_est_2015 3 hours ago

How a model is trained is different than how a model is constructed. A model’s construction defines its fundamental limitations, e.g. a linear regressor will never be able to provide meaningful inference on exponential data. Depending on how you train it, though, you can get such a model to provide acceptable results in some scenarios.

Mixing the two (training and construction) is rhetorically convenient (anthropomorphization), but holds us back in critically assessing a model’s capabilities.

▲ hackinthebochs 2 hours ago | parent [-]

Linear regression has well characterized mathematical properties. But we don't know the computational limits of stacked transformers. And so declaring what LLMs can't do is wildly premature.

▲ datsci_est_2015 2 hours ago | parent [-]

> And so declaring what LLMs can't do is wildly premature.

The opposite is true as well. Emergent complexity isn’t limitless. Just like early physicists tried to explain the emergent complexity of the universe through experimentation and theory, so should we try to explain the emergent complexity of LLMs through experimentation and theory.

Specifically not pseudoscience, though.

▲ hackinthebochs an hour ago | parent [-]

Sure, that's true as well. But I don't see this as a substantive response given that the only people making unsupported claims in this thread are those trying to deflate LLM capabilities.

▲ datsci_est_2015 39 minutes ago | parent [-]

So, to review this thread

  - OP asked for someone to make a logical argument for the separation of “training” from “model”
  - I made the argument
  - You cherry picked an argument against my specific example and made an appeal to emergent complexity
  - I pointed out that emergent complexity isn’t limitless
  - “the only people making unsupported claims in this thread are those trying to deflate LLM capabilities”