@inproceedings{gao-etal-2025-bliss,
    title = "{BL}i{SS}: Evaluating Bilingual Learner Competence in Second Language Small Language Models",
    author = "Gao, Yuan  and
      Salhan, Suchir  and
      Caines, Andrew  and
      Buttery, Paula  and
      Sun, Weiwei",
    editor = "Charpentier, Lucas  and
      Choshen, Leshem  and
      Cotterell, Ryan  and
      Gul, Mustafa Omer  and
      Hu, Michael Y.  and
      Liu, Jing  and
      Jumelet, Jaap  and
      Linzen, Tal  and
      Mueller, Aaron  and
      Ross, Candace  and
      Shah, Raj Sanjay  and
      Warstadt, Alex  and
      Wilcox, Ethan Gotlieb  and
      Williams, Adina",
    booktitle = "Proceedings of the First BabyLM Workshop",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.babylm-main.13/",
    pages = "160--174",
    ISBN = "TODO",
    abstract = "Cross-lingual extensions of the BabyLM Shared Task beyond English incentivise the development of Small Language Models that simulate a much wider range of language acquisition scenarios, including code-switching, simultaneous and successive bilingualism and second language acquisition. However, to our knowledge, there is no benchmark of the formal competence of cognitively-inspired models of L2 acquisition, or \textbf{L2LMs}. To address this, we introduce a \textbf{Benchmark of Learner Interlingual Syntactic Structure (BLiSS)}. BLiSS consists of 1.5M naturalistic minimal pairs dataset derived from errorful sentence{--}correction pairs in parallel learner corpora. These are systematic patterns {--}overlooked by standard benchmarks of the formal competence of Language Models {--} which we use to evaluate L2LMs trained in a variety of training regimes on specific properties of L2 learner language to provide a linguistically-motivated framework for controlled measure of the interlanguage competence of L2LMs."
}