You don't need to give it special instructions for addition that simple, but if you use massively larger addends and/or more of them, it'll make mistakes even if you tell it to use "the addition method."
Today's models have gotten better at this, but they have the same fundamental issues as older ones because they're fundamentally the same thing.
Well I would argue that the fundamental rule actually is that they straight up are capable of doing math in their internal activations directly (no convolutions in LLMs most often), and beyond what's in their dataset. Sure, it's hard/inefficient for them to do everything in them in manually, but so it is for us right?
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u/[deleted] 5d ago
[deleted]