alt Hacker NewsAs an MLE I feel these takes are too reductionist.
You could say the (nearly) same thing about search. And content recommendation. And clustering. And topic modeling. And outlier detection. And spam filtering. And image diffusion. And dimension reduction. And...
There's a lot in common between these things, but there's also a lot cool and different!
For transformers in particular, it's pretty cool that you get some WILD emergent properties simply from scaling up.
So yes, it's just a next token predictor, but I'm just a bundle of nerves and meat. I don't get a lot out of those descriptions.
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Obligatory link to the classic copy-pasta:
> They're Made out of Meat
https://web.mit.edu/people/dpolicar/writing/prose/text/think...
It's a little different than that. Your bundle of nerves and meat is not static. It changes over time.
To me the heart of the "next token predictor" is that the distributions are static. You can manipulate what you feed into it through context (and a lot of interesting engineering has been applied there through CoT and other techniques to manipulate the prompt). But these models as implemented will never be able try things and learn from mistakes or adapt. They are a set of weights frozen in time. A set of distributions derived from the original data that created them.
Some concrete facts about LLMs are explained by their next token predictor nature. Every time it says "wait, that's wrong." instead of generating the correct thing the first time.