@inproceedings{lv-etal-2025-dutir,
    title = "{DUTIR} at {S}em{E}val-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News",
    author = "Lv, Tengxiao  and
      Li, Juntao  and
      Liu, Chao  and
      Kang, Yiyang  and
      Luo, Ling  and
      Sun, Yuanyuan  and
      Lin, Hongfei",
    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/corrections-2025-08/2025.semeval-1.25/",
    pages = "174--179",
    ISBN = "979-8-89176-273-2",
    abstract = "We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model{'}s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian)."
}