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Impressive work. I wonder what would be the best way to use 8k embeddings. It’s a lot of information to keep in a vector, so things like “precision” of the emb
by omneity 3y ago
Impressive work.
I wonder what would be the best way to use 8k embeddings. It’s a lot of information to keep in a vector, so things like “precision” of the embedding space and its ability to distinguish very similar large documents will be key.
Maybe it can be useful for coarse similarity matching, for example to detect plagiarism?
- sroussey 3y ago8K is the context length. Their vector dimension size is actual much smaller, which is great for a number of use cases, though maybe not the ones you are thinking about.
- deleted 3y ago[deleted]
- omneity 3y agoYes that’s also how I understood it. Maybe it was ambiguously expressed, but I mean “8k tokens as input is a lot of information to encode”