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Such a confusing comment, because when I enter the text from case study #1 into Deepl, it's very clearly much worse than what Claude or GPT4o can come up with (the first few lines from Deepl are: "With his expositions to all the Tables, particularly of the quality of the countries, et of the most notable things, to be found in them. Which Tables, can be, and t are taught to reduce' together" and so on).

Likewise with using Google translate on both case studies #1 and #2 - the results are self-evidently far worse. In both cases there were multiple errors in each line and in case study #2 it was entirely unable to transcribe or translate the title line. If you see this, please email me at bebreen [at] ucsc dot edu to share the better results you are seeing because I genuinely am interested and open to using alternative tools - I just am not seeing what you are seeing, apparently.

In terms of typos not changing the meaning, yes naturally a real human needs to double check absolutely everything if it's being used in research. We agree on that - the point is simply that this significantly speeds up the initial research process, not that it replaces the expertise necessary to, for instance, double check that a name or year is transcribed correctly. A huge amount of historical research is simply about skimming through documents looking for relevent info to zero in on - this is where LLMs can really help.



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