AI brief
A question on AI Stack Exchange asks how to find the top k embedding vectors that are similar to one vector A and dissimilar to another vector B, proposing a scoring function that subtracts the cosine similarity to B from the cosine similarity to A.
Written by AI from AI Stack Exchange's published text.
Source
“Given vector A and vector B, which would be the better approach for getting top k embedding vectors such that they are similar to embedding vector A and dissimilar to vector B.”
Interpretation or community commentary rather than straight reporting.
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