@inproceedings{bazdyrev-etal-2025-transforming,
    title = "Transforming Causal {LLM} into {MLM} Encoder for Detecting Social Media Manipulation in Telegram",
    author = "Bazdyrev, Anton  and
      Bashtovyi, Ivan  and
      Havlytskyi, Ivan  and
      Kharytonov, Oleksandr  and
      Khodakovskyi, Artur",
    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-1.13/",
    pages = "112--119",
    ISBN = "979-8-89176-269-5",
    abstract = "We participated in the Fourth UNLP shared task on detecting social media manipulation in Ukrainian Telegram posts, addressing both multilabel technique classification and token-level span identification. We propose two complementary solutions: for classification, we fine-tune the decoder-only model with class-balanced grid-search thresholding and ensembling. For span detection, we convert causal LLM into a bidirectional encoder via masked language modeling pretraining on large Ukrainian and Russian news corpora before fine-tuning. Our solutions achieve SOTA metric results on both shared task track. Our work demonstrates the efficacy of bidirectional pretraining for decoder-only LLMs and robust threshold optimization, contributing new methods for disinformation detection in low-resource languages."
}