@inproceedings{mudryi-ignatenko-2025-precision,
    title = "Precision vs. Perturbation: Robustness Analysis of Synonym Attacks in {U}krainian {NLP}",
    author = "Mudryi, Volodymyr  and
      Ignatenko, Oleksii",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.unlp-1.15/",
    pages = "131--146",
    ISBN = "979-8-89176-269-5",
    abstract = "Synonym-based adversarial tests reveal fragile word patterns that accuracy metrics overlook, while virtually no such diagnostics exist for Ukrainian, a morphologically rich and low{-}resource language. We present the first systematic robustness evaluation under synonym substitution in Ukrainian. Adapting TextFooler and BERT{-}Attack to Ukrainian, we (i) adjust a 15000{-}entry synonym dictionary to match proper word forms; (ii) integrate similarity filters; (iii) adapt masked{-}LM search so it generates only valid inflected words. Across three text classification datasets (reviews, news headlines, social{-}media manipulation) and three transformer models (Ukr{-}RoBERTa, XLM{-}RoBERTa, SBERT), single{-}word swaps reduce accuracy by up to 12.6, while multi{-}step attacks degrade performance by as much as 40.27 with around 112 model queries. A few{-}shot transfer test shows GPT{-}4o, a state{-}of{-}the{-}art multilingual LLM, still suffers 6.9{--}15.0 drops on the same adversarial samples. Our results underscore the need for sense{-}aware, morphology{-}constrained synonym resources and provide a reproducible benchmark for future robustness research in Ukrainian NLP."
}