@inproceedings{karpachev-etal-2025-yandex,
    title = "{Y}andex Submission to the {WMT}25 General Machine Translation Task",
    author = "Karpachev, Nikolay  and
      Enikeeva, Ekaterina  and
      Popov, Dmitry  and
      Bulgakov, Arsenii  and
      Panteleev, Daniil  and
      Ulianov, Dmitrii  and
      Kryukov, Artem  and
      Mekhraliev, Artem",
    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-1.50/",
    pages = "740--752",
    ISBN = "979-8-89176-341-8",
    abstract = "This paper describes Yandex submission to the WMT25 General Machine Translation task. We participate in English-to-Russian translation direction and propose a purely LLM-based translation model. Our training procedure comprises a training pipeline of several stages built upon YandexGPT, an in-house general-purpose LLM. In particular, firstly, we employ continual pretraining (post-pretrain) for MT task for initial adaptation to multilinguality and translation. Subsequently, we use SFT on parallel document-level corpus in the form of P-Tuning. Following SFT, we propose a novel alignment scheme of two stages, the first one being a curriculum learning with difficulty schedule and a second one - training the model for tag preservation and error correction with human post-edits as training samples. Our model achieves results comparable to human reference translations on multiple domains."
}