SOURCE-LINKED INTELLIGENCE

Last Translation Benchmark

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

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 not problem-free, because it often lacks reproducibility, objectivity, and scalability. Overall, this prevents us from tracking objective progress in the field and identifying pathways for improvemen

Read original source ↗ Open in workspace

recordType

paper

region

Global