Junxiao Han


2025

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ProvBench: A Benchmark of Legal Provision Recommendation for Contract Auto-Reviewing
Xiuxuan Shen | Zhongyuan Jiang | Junsan Zhang | Junxiao Han | Yao Wan | Chengjie Guo | Bingcheng Liu | Jie Wu | Renxiang Li | Philip S. Yu
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.