"Ithaca restored artificially produced gaps in ancient texts with 62% accuracy, compared with 25% for human experts. But experts aided by Ithaca’s suggestions had the best results of all, filling gaps with an accuracy of 72%. Ithaca also identified the geographical origins of inscriptions with 71% accuracy, and dated them to within 30 years of accepted estimates."
and
"[Using] an RNN to restore missing text from a series of 1,100 Mycenaean tablets ... written in a script called Linear B in the second millennium bc. In tests with artificially produced gaps, the model’s top ten predictions included the correct answer 72% of the time, and in real-world cases it often matched the suggestions of human specialists."
Obviously 62%, 72%, 72% in ten tries, etc. is not sufficient by itself. How do scholars use these tools? Without some external source to verify the truth, you can't know if the software output is accurate. And if you have some reliable external source, you don't need the software.
Obviously, they've thought of that, and it's worth experimenting with these powerful tools. But I wonder how they've solved that problem.