@inproceedings{ao-etal-2024-ji,
    title = "基于{C}hat{GPT}查询改写的文档检索方法(Document Retrieval Method Based on {C}hat{GPT} Query Rewriting)",
    author = "Ao, Li  and
      Xinhui, Tu  and
      Yinghao, Xiong",
    editor = "Sun, Maosong  and
      Liang, Jiye  and
      Han, Xianpei  and
      Liu, Zhiyuan  and
      He, Yulan",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.ccl-1.2/",
    pages = "15--26",
    language = "zho",
    abstract = "``查询改写是一种通过优化查询从而提高检索结果质量的技术。传统的基于伪相关反馈的方法受限于伪相关文档的质量。本文提出了一种基于ChatGPT查询改写的文档检索方法。这种方法不依赖伪相关文档,可以避免伪相关文档质量不高的问题。首先,利用BM25模型进行检索,获得初次检索结果集;同时借助ChatGPT生成新查询;然后分别将原始查询和新查询作为输入,利用重排模型对初次检索结果集进行重排,得到各自的文档相关性得分;最后,将两个查询的文档相关性得分进行融合,得到最终的文档得分。在多个检索测试集上的实验结果表明,相比于基准模型,基于ChatGPT查询改写的文档检索方法在nDCG@10指标上平均提升了约4.5个百分点。''"
}