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Comment on A polynomial autoencoder beats PCA on transformer embeddings

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I'm just a casual LLM user, but your description of the anisotropy made me think about the recent work on KV cache quantization techniques such as TurboQuant where they apply a random rotation on each vector before quantizing, as I understood it precisely to make it more isotropic.

But for RAG that might be too much work per vector?

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