Paper recorded by Signals 4 on 2026-09-03 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04
Category: cs.CL · 自然语言处理 · first seen 2026-09-04
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation are approaching saturation. Further, automatic translation metrics are unreliable, vulnerable to reward-hacking, and provide unactionable assessments. Even gold human evaluation is