利用检索增强大语言模型优化工业合同管理
Streamlining Industrial Contract Management with Retrieval-Augmented LLMs
AI总结：
针对工业合同管理自动化中标注数据不足、非结构化存量合同多的难题，提出基于RAG的模块化合同管理框架，在真实低资源场景下问题修订识别与优化准确率超80%，可加速合同修订流程。
摘要：
合同管理工作涉及审核与协商各类条款，即界定协议权利、义务及约定条件的单项条目。该过程中，相关方会提出条款修订并反复打磨，其中部分修订可能存在问题或不被接受。由于标注数据稀缺、非结构化存量合同数量庞大，实现该工作流自动化存在较高难度。本文提出一种模块化框架，通过检索增强生成（RAG）管道优化合同管理流程。该系统整合合成数据生成、语义条款检索、可接受性分类、基于奖励的对齐等模块，可标记存在问题的修订并生成优化后的替代版本。该系统与行业合作伙伴联合开发并完成评估，在问题修订识别与优化两项任务上准确率均超过80%，在真实低资源场景下表现优异，为加速合同修订工作流提供了可行方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
