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I can’t help but wonder how well more traditional approaches would do with this. Something like a map of statements to actions, with fuzzy search - then remove what used to be the labour intensive part of this by handing it to a decent llm to generate the sentences.

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That's a really good point and I think it's not yet clear how well, say, 8-30MB worth of regexs with accompanying algorithmic structure would do on these tasks. I would imagine they do quite well on a well defined task, but it would be much harder to then adapt this set to a new domain. A big part of Needle's promise is how easy it is to finetune. Ultimately I think the two approaches can be more complimentary to each other, rather than choosing only one (see triggers!).

I think a good "traditional" approach would look like a BM25 algorithm over an index of trigger phrases for each category, sitting behind a majority-vote classifier. The "fine tuning" would be done by reindexing the data, generating different/new phrases, and tuning the classification threshold.

I think we might look into creating a baseline like this for our future models

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