@inproceedings{yueyi-yuxuan-2024-chinese,
    title = "{C}hinese Parataxis Graph({CPG}) Parsing Based on Large Language Models",
    author = "YueYi, Sun  and
      Yuxuan, Wang",
    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-3.6/",
    pages = "51--61",
    language = "eng",
    abstract = "{\textquotedblleft}This paper presents the work submitted for the 23rd China National Conference on Computational Linguistics(Evaluation Workshop)(CCL24-Eval), focusing on the Chinese Parataxis Graph (CPG) Parsing task. CPG represents Chinese natural language hierarchically through relational triplets, providing a consistent representation for linguistic units of varying levels. Our approach has used large-scale language models through full fine-tuning, achieving the result with F1 value at 71.6{\%} in the contest and 74.76{\%} after the contest. Furtehrmore, our team has proposed a combined model that integrates multiple LoRA fine-tuned medium-scale models after the contest. This approach is able to minimize the time and space consumption while keeping the performance of CPG construction task relatively high.{\textquotedblright}"
}