No Text Needed: Forecasting MT Quality and Inequity from Fertility and Metadata

Researchers demonstrate that machine translation quality can be accurately predicted without running translation systems, using only token fertility ratios, token counts, and linguistic metadata. The study achieved R² scores of 0.66-0.72 when forecasting GPT-4o translation performance across 203 languages in the FLORES-200 benchmark.