With impact of vector magnitudes on similarity score, are there scenarios where using dot-product might accidentally exaggerate similarity, like for vectors with large magnitudes?
Or are web content/passage embeddings too few dimensions to worry about that?
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Absolutely. If direction is more important than intensity, use cosine similarity or normalize embeddings before computing dot-product.