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Vicki Boykis GitHub
what_are_embeddings — A deep dive into embeddings starting from fundamentals
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korrents.com
Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them.Their words
However, larger models already have sufficient capacity where these extra embeddings may not help that much.
23 January
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5 June 2025
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the text embeddings across LLMs appear to largely converge on a "universal geometry" despite differing architectures, parameter counts, and training sets.
19 June 2024
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korrents.com
Vector embeddings will not solve search: a decades-old term-frequency algorithm still beats most of them at ranking.Their words
There is an algorithm called BM25 precisely for this, which is a more sophisticated version of TF-IDF. TF-IDF is term frequency times inverse document frequency, a very old-school information retrieval system that just works actually really well even today. And BM25 is a more sophisticated version of that, that is still beating most embeddings on ranking.
28 July 2022
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