@inproceedings{liu-etal-2025-team,
    title = "Team {QUST} at {S}em{E}val-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing",
    author = "Liu, Jiyan  and
      Liu, Youzheng  and
      Wang, Taihang  and
      Xu, Xiaoman  and
      Wang, Yimin  and
      Jiang, Ye",
    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-1.139/",
    pages = "1052--1056",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025{\_}Task10"
}