用检索增强生成技术自动识别并优化合同修订中的问题条款。
Streamlining Industrial Contract Management with Retrieval-Augmented LLMs
- 构建检索增强生成流程，融合合成数据与语义检索提升准确性。
- 在真实工业场景中实现超80%的错误识别与优化准确率。
- 适合法律科技公司及企业法务部门快速处理海量合同修订。
合同管理涉及审查和协商条款，这些条款定义了各方的权利、义务和协议条件。在这一过程中，需反复提出并修改条款，其中部分修订可能存在风险或不可接受。由于标注数据稀缺且历史合同高度非结构化，自动化该流程极具挑战。本文提出一种模块化框架，通过检索增强生成（RAG）管道简化合同管理。系统整合合成数据生成、语义条款检索、可接受性分类与基于奖励的对齐机制，用于识别问题修订并生成优化建议。与产业合作伙伴联合开发并评估，系统在真实低资源环境下实现超过80%的准确率，显著加速合同修订流程。
原文摘要 · Abstract (English)
Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the abundance of unstructured legacy contracts. In this paper, we present a modular framework designed to streamline contract management through a retrieval-augmented generation (RAG) pipeline. Our system integrates synthetic data generation, semantic clause retrieval, acceptability classification, and reward-based alignment to flag problematic revisions and generate improved alternatives. Developed and evaluated in collaboration with an industry partner, our system achieves over 80% accuracy in both identifying and optimizing problematic revisions, demonstrating strong performance under real-world, low-resource conditions and offering a practical means of accelerating contract revision workflows.
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