@inproceedings{wen-etal-2024-ren,
    title = "人类思维指导下大小模型协同决策的中文修辞识别与理解方法",
    author = "Wen, Wang  and
      Siyi, Tang  and
      Dong, Yu  and
      Pengyuan, Liu",
    editor = "Lin, Hongfei  and
      Tan, Hongye  and
      Li, Bin",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.ccl-3.27/",
    pages = "240--252",
    language = "zho",
    abstract = "{\textquotedblleft}CCL24-Eval任务6提出了一个多层次、细粒度中小学作文修辞识别与理解任务。针对任务特点,本文提出了人类思维指导下大小模型协同决策的中文修辞识别与理解方法。该方法根据人类在面对修辞识别和理解任务时的处理思路,将任务顺序重新定义,并分别选取大小语言模型,使每个步骤的实现效果均达到局部最优,以局部最优达到整体任务的最优效果。结果表明,本文提出的方法能够有效对修辞进行识别与理解,在三个赛道上相较于Baseline方法分别提升了13.54、4.03、57.11。{\textquotedblright}"
}