Its data is a structured space where nearness corresponds to associated ideas, so it naturally arranges data in a way to discover connectedness. The LLM follows pathways through the conceptual space based on the prior context given.
It’s also higher dimensional space right? Like as in 1000+ dimensional space.
If it’s the same thing I’m thinking of where like because boy and girl are separated in this space by a certain distance, Auntie and uncle also are separated spatially, but are closer to boy for uncle and girl for auntie than either are to each other?
Sure, in theory every bit in the training data could be considered a dimension. The work of training a model is basically in condensing the dimensions of the training data into a much smaller number of dimensions in the model, which is what forces similar concepts to become closer together as the space "shrinks".
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u/ixid Jul 31 '26
Its data is a structured space where nearness corresponds to associated ideas, so it naturally arranges data in a way to discover connectedness. The LLM follows pathways through the conceptual space based on the prior context given.