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by macksd
768 days ago
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>> Our study reaffirms the limitations of LLM tokenization Because they used data that needs to be tokenized differently, and didn't really tune the models for use on that data. That's not really a limit of LLM tokenization per se. >> We did not evaluate strategies known to improve LLM performance, including ... retrieval augmented generation Which is a shame because this is exactly the kind of use case RAG is supposed to be good for and they largely observed problems it's supposed to help with. Looking at the authors, it seems to me they're all subject matter experts in medicine and digital medicine, but their conclusion is the one in support of medical professionals and they really don't seem to have tried that hard to get good deep learning results. I've had nightmares every time I've seen a doctor in the US, frequently because of things not being coded correctly. So honestly I'd just love to see a rigorous study of how often the human staff is messing it up too. |
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You know the Physician cartel is going to find some nonsensical reasoning to be anti-LLM even when LLMs will diagnose correctly more often.
When every other industry is finding uses, 'medical can't, its too hard', is going to be a normal line from the industry that still uses Faxes.