@inproceedings{fenu-etal-2025-demon,
    title = "{DEMON} at {S}em{E}val-2025 Task 10: Fine-tuning {LL}a{MA}-3 for Multilingual Entity Framing",
    author = "Fenu, Matteo  and
      Sanguinetti, Manuela  and
      Atzori, Maurizio",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.192/",
    pages = "1456--1464",
    ISBN = "979-8-89176-273-2",
    abstract = "This study introduces a methodology centred on Llama 3 fine-tuning for the classification of entities mentioned within news articles, based on a predefined role taxonomy. The research is conducted as part of SemEval-2025 Task 10, which focuses on the automatic identification of narratives, their classification, and the determination of the roles of the relevant entities involved. The developed system was specifically used within Subtask 1 on Entity Framing. The approach used is based on parameter-efficient fine-tuning, in order to minimize the computational costs while maintaining reasonably good model performance across all datasets and languages involved.The model achieved promising results on both the development and test sets. Specifically, during the final evaluation phase, it attained an average accuracy of 0.84 on the main role and an average Exact Match Ratio of 0.41 in the prediction of fine-grained roles across all the five languages involved, i.e. Bulgarian, English, Hindi, Portuguese and Russian. The best performance was observed for English (3rd place out of 32 participants), on a par with Hindi and Russian. The paper provides an overview of the system adopted for the task and discusses the results obtained."
}