I think that's why calculating and showing the accuracy metrics (like WER) against known good translations, is useful. You can even highlight words that the mod

I think that's why calculating and showing the accuracy metrics (like WER) against known good translations, is useful. You can even highlight words that the model struggles with, giving the reader useful context. Translation models are getting better all the time - it's a weird artefact of transformer architectures that got missed in the GenAI hype, that they're pretty great at translation, especially across languages with smaller training corpuses - but you should definitely know if the text you're reading is only likely to be 90% "correctly" translated.