@inproceedings{yin-etal-2025-ji,
    title = "基于双系统推理框架的法律判决研究",
    author = "Yin, Shengdi  and
      Bai, Zewen  and
      Lin, Hongfei  and
      Yang, Liang",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    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-1.37/",
    pages = "500--512",
    abstract = "``法律判决预测是法律人工智能领域的一项重要任务。本文提出了一种基于外部知识的可解释性双系统推理框架,来解决现有方法在刑期预测任务中精度不高且可解释性不强的问题。该框架借鉴认知科学领域的双系统理论,利用大型语言模型的文本理解和生成能力,模拟人类法官处理案件时的决策过程,最终给出具有清晰推理路径的刑期预测结果。此外,通过构建一个高质量思考增强数据集和一个外部法条知识库,提升了模型的解释能力并且有效地抑制法条判断模型出现法条幻觉。实验结果表明,该框架显著提升了CAIL-small和CAIL-big数据集中刑期预测子任务上的精度和可解释性。''"
}