@inproceedings{li-etal-2025-ccl25,
    title = "{CCL}25-Eval任务四系统报告:宏观模式提示与高效微调在叙实性推理中的应用",
    author = "Li, Zequn  and
      Zhong, Yuanhao  and
      Chai, Chengliang",
    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-2.14/",
    pages = "118--127",
    abstract = "``本文研究了利用大语言模型进行谓词引导的叙实性推理任务。在不微调场景下,针对Gemini 2.5 Pro模型,我们构建了基于谓词类型的思维链(CoT)提示,并创新性地让模型学习整个带答案的样本集以归纳宏观模式和规则,最终形成高效的提示词模板。在微调场景下,我们选用Qwen3-32b模型,利用llama factory进行LoRA微调,并使用llama.cpp完成模型向gguf格式的转换、量化及Ollama部署。实验结果展示了所提方法的有效性,其中在不微调赛道上,基于宏观模式提示的方法取得了94.01{\%}的准确率;在微调赛道上,基于微调模型的系统取得了92.61{\%}的准确率。''"
}