@inproceedings{shen-etal-2025-provbench,
    title = "{P}rov{B}ench: A Benchmark of Legal Provision Recommendation for Contract Auto-Reviewing",
    author = "Shen, Xiuxuan  and
      Jiang, Zhongyuan  and
      Zhang, Junsan  and
      Han, Junxiao  and
      Wan, Yao  and
      Guo, Chengjie  and
      Liu, Bingcheng  and
      Wu, Jie  and
      Li, Renxiang  and
      Yu, Philip S.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.312/",
    pages = "6240--6254",
    ISBN = "979-8-89176-251-0",
    abstract = "Contract review is a critical process to protect the rights and interests of the parties involved. However, this process is time-consuming, labor-intensive, and costly, especially when a contract faces multiple rounds of review. To accelerate the contract review and promote the completion of transactions, this paper introduces a novel benchmark of legal provision recommendation and conflict detection for contract auto-reviewing (ProvBench), which aims to recommend the legal provisions related to contract clauses and detect possible legal conflicts. Specifically, we construct the first Legal Provision Recommendation Dataset: ProvData, which covers 8 common contract types. In addition, we conduct extensive experiments to evaluate ProvBench on various state-of-the-art models. Experimental results validate the feasibility of ProvBench and demonstrate the effectiveness of ProvData. Finally, we identify potential challenges in the ProvBench and advocate for further investigation."
}