That book probably doesn’t go much further than neural networks with 1 hidden layer. Maybe 2 hidden layers at most.
IMO, statistics is about explaining data. Regression is useful to explain how parameters relate to each others. Statistics that don’t help us understand data isn’t useful statistics.
Modern machine learning has strayed far away from data explanation. Now it’s common to deal with more than a dozen hidden layers. It might have roots in statistics, but mostly it’s about brute forcing any curve to the data. It doesn’t help us understanding the data better, but at least we have approximated some function.
If you have any ideas about how statistics are at the bottom of LLMs, you are probably thinking about some other ML technique.
It might have roots in statistics
Care to reiterate?
Just because wheels have roots in horse wagons doesn’t mean cars are horse wagons
That’s where the almost comes in. Unfortunately, there are many traps for the unwary stochastic parrot.
Training a neural net can be seen as a generalized regression analysis. But that’s not where it comes from. Inspiration comes mainly from biology, and also from physics. It’s not a result of developing better statistics. Training algorithms, like Backprop, were developed for the purpose. It’s not something that the pioneers could look up in a stats textbook. This is why the terminology is different. Where the same terms are used, they don’t mean quite the same thing, unfortunately.
Many developments crucial for LLMs have no counterpart in statistics, like fine-tuning, RLHF, or self-attention. Conversely, what you typically want from a regression - such as neatly interpretable parameters with error bars - is conspicuously absent in ANNs.
Any ideas you have formed about LLMs, based on the understanding that they are just statistics, are very likely wrong.
“such as neatly interpretable parameters”
Hahaha, hahahahahaha.
Hahahahaha.
If parameters aren’t neatly interpretable then it’s bad statistics. You’ve learned nothing about the general structure of the data.
Linear regression models are often great tools for explaining the structure of the data. You can directly see which parts of the input are more important for determining the output. You have very little of that when using neural networks with more than 1 hidden layer.
