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I wonder if text embeddings and semantic similarity would be effective here?
This was fun to read “ Fun fact: Translating French and Spanish claims into German first improved technical accuracy—an unexpected perk of Germany’s automotive dominance.”
Given that it was inside a 9-step text preprocessing pipeline, it would be surprising if the AI had that much autonomy.
* already known as SotA for text classification and similarity
back in 2023
* natively multi-lingualI wonder if text embeddings and semantic similarity would be effective here?
I wonder if text embeddings and semantic similarity would be effective here?
> We tried multiple vectorization and classification approaches. Our data was heavily imbalanced and skewed towards negative cases. We found that TF-IDF with 1-gram features paired with XGBoost consistently emerged as the winner.
Hmm, why was their starting point not something like BERT: * already known as SotA for text classification and similarity back in 2023 * natively multi-lingual
But no, they want to pay $0.1 per request to recognize if a photo has a person in it by asking a multimodal LLM deployed across 8x GPUs, for some reason, instead of just spending some hours with CLIP and run it effectively even on CPU.
Did the author exactly define "Nova Lite" somewhere in there?
Looks like they were limited by AWS Bedrock options.
This was fun to read “ Fun fact: Translating French and Spanish claims into German first improved technical accuracy—an unexpected perk of Germany’s automotive dominance.”
I wonder how they came up with that. Was it a human idea, or did the AI stumble upon it? Given that it was inside a 9-step text preprocessing pipeline, it would be surprising if the AI had that much autonomy.
Over the past couple of years people have made attempts with NLP (lets say standard ML workflows) but NLP and word temperature scores are hard to integrate into a reliable data pipeline much less a operational review workflow.
Enter LLM's, the world is a data gurus oyster for building an detection system on warranty claims. Passing data to Prompted LLM's means capturing and classifying records becomes significantly easier, and these data applications can flow into more normal analytic work streams.