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We demonstrate that the weight matrices of multiple large language models sharing the same architecture but trained for different purposes (code generation vs conversational instruction-following) occupy a shared low-rank manifold. By computing a joint eigenspace (via SVD) across model weights, we factor N separate mod...
We demonstrate that the weight matrices of multiple large language models sharing the same architecture but trained for different purposes (code generation vs conversational instruction-following) occupy a shared low-rank manifold. By computing a joint eigenspace (via SVD) across model weights, we factor N separate mod...
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