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Couldn't you just compare the similarity of the embeddings? I imagine that would work in the vast majority of cases and save a lot of LLM calls.


That's a good idea, the deduplication criterion is easy to change, using an llm is faster to get started, but after a while a corpus of decisions is created and can be used to either select another mechanism, or e.g. train one on top of bert embeddings.




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