@inproceedings{oji-kunz-2025-tune,
    title = "How to Tune a Multilingual Encoder Model for {German}ic Languages: {A} Study of {PEFT,} Full Fine-Tuning, and Language Adapters",
    author = "Oji, Romina  and
      Kunz, Jenny",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nodalida-1.47/",
    pages = "433--439",
    ISBN = "978-9908-53-109-0",
    abstract = "This paper investigates the optimal use of the multilingual encoder model mDeBERTa for tasks in three Germanic languages {--} German, Swedish, and Icelandic {--} representing varying levels of presence and likely data quality in mDeBERTas pre-training data. We compare full fine-tuning with the parameter-efficient fine-tuning (PEFT) methods LoRA and Pfeiffer bottleneck adapters, finding that PEFT is more effective for the higher-resource language, German. However, results for Swedish and Icelandic are less consistent. We also observe differences between tasks: While PEFT tends to work better for question answering, full fine-tuning is preferable for named entity recognition. Inspired by previous research on modular approaches that combine task and language adapters, we evaluate the impact of adding PEFT modules trained on unstructured text, finding that this approach is not beneficial."
}