Dominik Macháček

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.

Text-only and multimodal LLMs achieve only half the precision of humans in labeling speech translation errors, highlighting a significant gap in current evaluation methodologies.