@inproceedings{sushko-etal-2025-skipclm,
    title = "{S}kip{CLM}: Enhancing Crosslingual Alignment of Decoder Transformer Models via Contrastive Learning and Skip Connection",
    author = "Sushko, Nikita  and
      Panchenko, Alexander  and
      Tutubalina, Elena",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-srw.50/",
    pages = "517--528",
    ISBN = "979-8-89176-192-6",
    abstract = "This paper proposes SkipCLM, a novel method for improving multilingual machine translation in Decoder Transformers. We augment contrastive learning for cross-lingual alignment with a trainable skip connection to preserve information crucial for accurate target language generation. Experiments with XGLM-564M on the Flores-101 benchmark demonstrate improved performance, particularly for en-de and en-zh direction translations, compared to direct sequence-to-sequence training and existing contrastive learning methods. Code is available at: https://github.com/s-nlp/skipclm."
}