原始来源
LlamaIndex Blog ·
Logan Markewich reports on the team’s experiments with static embedding retrieval. He explains where MaxSim scoring and small convolutional adapters fell short, despite the speed of static embeddings.
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原始摘录
A better teacher is not a better student. When the student is too small to absorb what the teacher knows (i.e. a small conv model), teacher quality stops mattering.
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原始摘录
Don't score raw static token vectors with MaxSim. It's worse than the standard pooling, the scoring method is technically changing the training target. MaxSim assumes contextualized vectors and static embeddings are not contextual.
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原始摘录
Static embedding models can seem like a cheat code at first glance. Embedding a line of text is just a table lookup plus an average calculation. This translates to about 0.05 ms per text line on CPU, no transformer forward pass, it's WASM-friendly, and ~100× faster than even a small dense model.
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