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by viraptor
613 days ago
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It matches the training data. Whether the training data matches truth (and whether it's correctly understood - sarcasm included) is a completely separate thing. > The training does not perfectly align the model with truth, but 'orthogonal' Nitpicky, but the more dimensions you have, the easier it is for almost everything to be orthogonal. (https://softwaredoug.com/blog/2022/12/26/surpries-at-hi-dime...) That's why averaging embeddings works. |
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If you add two vectors that don't have a truth component (ie. are orthogonal to the truth), the resulting vector should be no closer to the truth. If you start with random weights and perform some operation on them such that the new weights have a higher likelihood of producing true statements, the operation must not have been orthogonal to the truth. Am I wrong there?