@inproceedings{li-etal-2025-ccl25-evalren,
    title = "{CCL}25-Eval任务四系统报告:基于多策略知识融合的叙实性推理方法研究",
    author = "Li, Hongyu  and
      Yang, Zhihui  and
      Hu, Renfen",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.19/",
    pages = "157--165",
    abstract = "``FIE2025任务旨在使用大语言模型对文本及相关假设进行叙实性推理。我们参加了微调和非微调两个赛道,分别在人工数据集和自然数据集上采用提示词优化和词表RAG策略融合语言学知识,并利用模型集成投票方法提升判断准确率。评测结果显示,我们的方法在非微调赛道取得了0.9351的成绩,在微调赛道取得了0.9261的成绩,均位列第三名。''"
}