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The use case for small models include sentiment and intent analysis, spam and abuse detection, and classifications of various sorts. Generally LLM are thought o
by nialse 2y ago
The use case for small models include sentiment and intent analysis, spam and abuse detection, and classifications of various sorts. Generally LLM are thought of as chat models but the output need not be a conversation per se.
- mhitza 2y agoMy impression was that text embeddings are better suited for classification. Of course the big caveat is that the embeddings must have "internalized" the semantic concept you're trying to map. From some article I have in my draft, experimenting with open source text embeddings: ./match venture capital purchase 0.74005488647684 sale 0.80926752301733 place 0.81188663814236 positive sentiment 0.90793311875207 negative sentiment 0.91083707598925 time 0.9108697315425 ./store sillicon valley ./match venture capital sillicon valley 0.7245139487301 purchase 0.74005488647684 sale 0.80926752301733 place 0.81188663814236 positive sentiment 0.90793311875207 negative sentiment 0.91083707598925 time 0.9108697315425 Of course you need to figure out what these black boxes understand. For example for sentiment analysis, instead of having it match against "positive" "negative" you would have the matching terms be "kawai" and "student debt". Depending how the text embedding internalized negatives and positives based on their training data.