@inproceedings{rajaee-etal-2025-empirical,
    title = "An Empirical Analysis of Machine Translation for Expanding Multilingual Benchmarks",
    author = "Rajaee, Sara  and
      Choenni, Rochelle  and
      Shutova, Ekaterina  and
      Monz, Christof",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.1/",
    pages = "1--30",
    ISBN = "979-8-89176-341-8",
    abstract = "The rapid advancement of large language models (LLMs) has introduced new challenges in their evaluation, particularly for multilingual settings. The limited evaluation data are more pronounced in low-resource languages due to the scarcity of professional annotators, hindering fair progress across languages. In this work, we systematically investigate the viability of using machine translation (MT) as a proxy for evaluation in scenarios where human-annotated test sets are unavailable. Leveraging a state-of-the-art translation model, we translate datasets from four tasks into 198 languages and employ these translations to assess the quality and robustness of MT-based multilingual evaluation under different setups. We analyze task-specific error patterns, identifying when MT-based evaluation is reliable and when it produces misleading results. Our translated benchmark reveals that current language selections in multilingual datasets tend to overestimate LLM performance on low-resource languages. We conclude that although machine translation is not yet a fully reliable method for evaluating multilingual models, overlooking its potential means missing a valuable opportunity to track progress in non-English languages."
}