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Ah right, so you like AI and don't care to understand how it works.

It doesn't "decide" anything or "need" any semantic. It derives the likelihood of the token, and "bearing" is likely to come after "load".

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Sure but the question is why "load" after X?

Because, for some high number of contexts, its likelihood comes out high in the big tree of multiplies that is claude's model. For some sets of 500 words (or whatever), the next word is "load". The classifier that decides which sets of 500 (or whatever) words is a prefix for "load" is returning "true" too often.

More-or-less the same principle, but scaled up massively, and with context-dependent probability conditioning maps.

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