@inproceedings{poritski-etal-2026-llm,
    title = "{LLM} as a Morphological Disambiguator for {B}elarusian: A Preliminary Study",
    author = "Poritski, Vladislav  and
      Volchek, Oksana  and
      Afanasev, Ilia",
    editor = "Ojha, Atul Kr.  and
      Sakti, Sakriani  and
      Soria, Claudia  and
      Melero, Maite  and
      McCrae, John P.  and
      Lignos, Constantine  and
      Liu, Chao-Hong  and
      Claramunt, German Rigau  and
      Rehm, Georg",
    booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
    month = may,
    year = "2026",
    address = "Palma, Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.sigul-1.4/",
    doi = "10.63317/3skazxbd27m8",
    pages = "42--48",
    abstract = "We explore the use of large language models (LLMs) for morphological disambiguation in Belarusian, a low-resource language. The pipeline has two stages: a rule-based analyzer generates candidate lemmas and grammatical tags, which an LLM then disambiguates in context. Initial evaluation of ChatGPT, Claude, and Gemini on a gold-standard sample shows high accuracy. We scale this approach to a 375K-word corpus using Gemini and compare the results against a neural baseline (Stanza). Manual review of discrepancies suggests that the LLM-based approach outperforms the baseline, offering a solution for corpus annotation in Belarusian."
}