公开观点

Logan Markewich

Source author · LlamaIndex

1 场访谈 · 3 条观点 · 3 个话题

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static embedding performance

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Static embeddings can be fast on CPU

Markewich reports that static embeddings take about 0.05 ms per text line on CPU, need no transformer forward pass, are WASM-friendly, and run about 100 times faster than a small dense model.

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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.
Exploring Static Embedding Retrieval

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model distillation

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model distillation

Teacher quality stops mattering when the student is too small

From the team’s experiments, Markewich concludes that a better teacher does not help when a small convolutional student lacks the capacity to absorb what the teacher knows.

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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.
Exploring Static Embedding Retrieval

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retrieval methodology

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retrieval methodology

Raw static token vectors are a poor fit for MaxSim

Markewich advises against scoring raw static token vectors with MaxSim: it performed worse than standard pooling in the team’s exploration, changes the training target, and assumes contextualized vectors that static embeddings do not provide.

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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.
Exploring Static Embedding Retrieval

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