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> it's not impressive that ChatGPT is simply regurgitating stuff it knows about. to me it is. unless i misunderstand LLMs, they're trained on data. i couldn't

> it's not impressive that ChatGPT is simply regurgitating stuff it knows about. to me it is. unless i misunderstand LLMs, they're trained on data. i couldn't train a neural network to figure out how to do y = x * 2. how is it able to recite with 100% accuracy (instead of sub-100% accuracy, aka hallucination) based on "i predict this next word/punctuation in the sentence should be the highest scoring token given the relevant content" imagine having their massive index and being able to serve relevant content just from a "postgres" or "redis" cache/database perspective. even looking up content at that scale 1:1 is "hard".

kolinko 3y ago
y = x * 2 is what you get on the most basic neural network. You show it a bunch of points for that function and it will train to predict the rest or, in other words - nns and llms do exactly that - predict/invent functions that meet the data points
- MuffinFlavored 3y ago
  When people say "oh this LLM is 13B parameters", how many parameters is "y = x * 2"? 1? How long/how many iterations would I have to train a 1 layer NN (and what kind of layers should even be used, it gets so confusing with the activation layers, layer types, etc.) to actually do y = x * 2 https://stats.stackexchange.com/questions/217703/can-a-deep-neural-network-approximate-multiplication-function https://stats.stackexchange.com/questions/217703/can-a-deep-...
  - kolinko 3y ago
    x2 or xy? x2 is one neuron and probably a dosen iterations max. xy may be a bit more complicated - like the stackoverflow explains 13B parameters - yes, it’s essentially 13B multiplications/additions (the most basic neuron is y=activation_function(x*weight+bias).
deleted 3y ago
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quixoticaxolotl 3y ago
Actually, you can find online examples that demonstrate how easily you can teach a neural network how to do y = x^2 https://stackoverflow.com/questions/70407674/how-to-teach-a-parabolic-function-to-a-neural-net https://stackoverflow.com/questions/70407674/how-to-teach-a-... Neural networks, from one perspective, are good at finding hyperplanes between data points from two different sets. You can teach a neural network how to recognize X belongs in your dataset of choice, so it's relatively easy to find other points in the dataset of y = x^2 or so with that building block. I am no expert, but mechanistic interpretability seems to make the most sense in understanding individual NN layers: each layer recognizes a different combination of features corresponding to a certain pattern and feedback from the layers below. At a scale of 40B+ parameters and 120K context windows, we start to lose track of what the machine is doing, but it's not inconceivable that with such large numbers it can regurgitate very large portions of text with 100% accuracy. Scale matters a lot.

kolinko 3y ago

y = x * 2 is what you get on the most basic neural network. You show it a bunch of points for that function and it will train to predict the rest or, in other words - nns and llms do exactly that - predict/invent functions that meet the data points

MuffinFlavored 3y ago
When people say "oh this LLM is 13B parameters", how many parameters is "y = x * 2"? 1? How long/how many iterations would I have to train a 1 layer NN (and what kind of layers should even be used, it gets so confusing with the activation layers, layer types, etc.) to actually do y = x * 2 https://stats.stackexchange.com/questions/217703/can-a-deep-neural-network-approximate-multiplication-function https://stats.stackexchange.com/questions/217703/can-a-deep-...
- kolinko 3y ago
  x2 or xy? x2 is one neuron and probably a dosen iterations max. xy may be a bit more complicated - like the stackoverflow explains 13B parameters - yes, it’s essentially 13B multiplications/additions (the most basic neuron is y=activation_function(x*weight+bias).

deleted 3y ago

[deleted]

quixoticaxolotl 3y ago

Actually, you can find online examples that demonstrate how easily you can teach a neural network how to do y = x^2 https://stackoverflow.com/questions/70407674/how-to-teach-a-parabolic-function-to-a-neural-net https://stackoverflow.com/questions/70407674/how-to-teach-a-... Neural networks, from one perspective, are good at finding hyperplanes between data points from two different sets. You can teach a neural network how to recognize X belongs in your dataset of choice, so it's relatively easy to find other points in the dataset of y = x^2 or so with that building block. I am no expert, but mechanistic interpretability seems to make the most sense in understanding individual NN layers: each layer recognizes a different combination of features corresponding to a certain pattern and feedback from the layers below. At a scale of 40B+ parameters and 120K context windows, we start to lose track of what the machine is doing, but it's not inconceivable that with such large numbers it can regurgitate very large portions of text with 100% accuracy. Scale matters a lot.