It's very far off, like "not even wrong" in the Pauli sense of the phrase.
There's a lot of abstractions one can have for this stuff, I think you're looking at that "text predictor" is one of them?
If you roll with that, then you're in a position where you're saying GPT-2 class LLMs were very close in 1960, because at the end of the day, it's just a dictionary lookup with a string key and a value of list<string> completions. That confuses instead of illuminates.
The trouble with decision trees for language modeling is that they overfit really hard. They don't do the magical generalization that makes LLMs interesting.
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I mean it's not far off from a super low quant LLM with limited params, like a 1bit quant LLM with low params XD
It's very far off, like "not even wrong" in the Pauli sense of the phrase.
There's a lot of abstractions one can have for this stuff, I think you're looking at that "text predictor" is one of them?
If you roll with that, then you're in a position where you're saying GPT-2 class LLMs were very close in 1960, because at the end of the day, it's just a dictionary lookup with a string key and a value of list<string> completions. That confuses instead of illuminates.
The trouble with decision trees for language modeling is that they overfit really hard. They don't do the magical generalization that makes LLMs interesting.