Naively, training the same model on multiple languages has interesting implications. On one hand, it may capture something "deeper" about language. On the oth

Naively, training the same model on multiple languages has interesting implications. On one hand, it may capture something "deeper" about language. On the other hand, it's likely to do great in general, but miss particularities of some language. Understanding the coverage of the training model seems a perennial problem. Is there any (shorthand) way to compare language model training corpora? Clearly if they use common subsets we have a literal comparison. I'm more interested in whether there's progress in characterizing corpora by speech styles, fluency, vocabulary sets, (noise) environment, emotionality, proposition types, etc. (btw: 25 minutes for a 9-minute segment on a 12-thread x86. Lots of jargon spelled as it sounds. Sentences capitalized but no punctuation. Overall good.)