Question

A localization team is building a foundation-model application that automatically translates product documentation from English into Spanish, French, and German. Before deploying, they want an automated, reference-based metric that scores how closely the model's machine translations match a set of human-produced reference translations. Which evaluation metric is most appropriate for this task?

Accepted Answer

BLEU score, which measures n-gram overlap between machine output and human reference translations

Answer

F1 score computed from precision and recall on classification labels

Answer

Root mean squared error (RMSE) between predicted and actual numeric values

Answer

Perplexity measured on the model's original pretraining corpus