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Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
Continued pretraining with a carefully balanced data mixture can substantially improve a multilingual LLM's performance on a low-resource language like Estonian without compromising its English capabilities.
