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>Self-attention is just a frequency distribution over a freq distribution. I don't know where you get that. But it's not my understanding. >It's literally tra
by hackinthebochs 3y ago
>Self-attention is just a frequency distribution over a freq distribution.
I don't know where you get that. But it's not my understanding.
>It's literally trained with P(B|A) as the objective....
This is a description of the objective, not a model or an algorithm. The algorithm is not learning frequency data. The algorithm tries to maximize P(B|A), but within this constraint there are a vast range of algorithms.
- AstralStorm 3y agoBut can rhis algorithm ever produce reasoning without learning a whole universe of possible inputs? Given the evidence that it fails to learn arithmetic, skips inference steps, misassigns symbols, I'd say likely not.
- hackinthebochs 3y agoReasoning is abstracted from particulars. So in principle what it needs to learn is a finite set of rules. There are good reasons that explain why current LLMs don't learn arithmetic and has odd failure modes: it's processing is feed-forward (non-recursive) with a fixed computational budget. This means that it in principle cannot learn general rules for arithmetic which involve unbounded carrying. But this is not an in principle limitation for LLMs or gradient descent based ML in general.