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> why not use LLMs for dataset creation or augmentation in order to train a classic supervised model? Or, as I mentioned in another comment, just use the embed
by PheonixPharts 3y ago
> why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
Or, as I mentioned in another comment, just use the embeddings directly. This also does a lot to remove the "cost (money and latency)" part of the problem since you can batch queries to be lightening fast and the dollar cost of generating the embeddings is effectively zero (~3000 pages of text per $1) for most traditional NLP tasks that require a vector representation.
- azinman2 3y agoEmbeddings only go so far. A lot of time meaning (especially implicit or contextual) occurs from reading larger portions / knowing related information that will simply not be captured in the embedding.
- daveguy 3y agoIf it's not captured in the embeddings the LLM wouldn't "know" it either, right?
- bugglebeetle 3y agoHow are you batching queries and search in a way that’s “lightning fast”? I’ve not found this to be the case for most vector DBs, especially the building the index part.