@inproceedings{basirat-2025-multilingual,
    title = "Multilingual Learning Strategies in Multilingual Large Language Models",
    author = "Basirat, Ali",
    editor = "Adelani, David Ifeoluwa  and
      Arnett, Catherine  and
      Ataman, Duygu  and
      Chang, Tyler A.  and
      Gonen, Hila  and
      Raja, Rahul  and
      Schmidt, Fabian  and
      Stap, David  and
      Wang, Jiayi",
    booktitle = "Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)",
    month = nov,
    year = "2025",
    address = "Suzhuo, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.mrl-main.34/",
    pages = "507--518",
    ISBN = "979-8-89176-345-6",
    abstract = "Despite the effective performance of multilingual large language models (LLMs), the mechanisms underlying their multilingual capabilities remain unclear. This study examines the intermediate representations of multilingual LLMs to determine if these models utilize human-like second language acquisition strategies: coordinate, sub-coordinate, or compound learning. Our investigations into the discriminative and generative aspects of these models indicate that coordinate learning is the dominant mechanism, with decoder-only models progressively developing distinct feature spaces for each language, while encoder-only models exhibit a mixture of coordinate and compound learning in their middle layers. We find little evidence for sub-coordinate learning. Moreover, the role of training data coverage in shaping multilingual representations is reflected in the fact that languages present in a model{'}s training data consistently exhibit stronger separation than those absent from it."
}