用图模型与强化学习自动分析合同依赖关系，提升审查效率。
GRAPH-GRPO-LEX: Contract Graph Modeling and Reinforcement Learning with Group Relative Policy Optimization
- 将合同转为语义图，节点和边表示条款与关系
- 通过奖励函数识别条款间直接及隐藏依赖
- 适合法律科技、智能合约开发人员使用
合同是结构复杂、包含显性与隐性依赖关系的正式文档，其起草与人工审查过程繁琐且易出错。本文提出一种新框架，将法律合同转化为结构化语义图，实现计算分析与数据驱动洞察。我们构建了详细的本体映射，将核心法律条款映射为图论中的节点与边。随后提出基于大语言模型（LLM）的强化学习框架，用于实体与关系的分割与提取。所提方法GRAPH-GRPO-LEX结合了LLM与群体相对策略优化（GRPO），通过精心设计的图度量奖励函数，可自动识别条款间的直接关系，并发现潜在依赖。引入门控GRPO机制，显著增强学习信号，使合同分析从线性人工阅读转变为可视化图结构，支持更动态的分析，为类似软件工程中“合同校验”（contract linting）奠定基础。
原文摘要 · Abstract (English)
Contracts are complex documents featuring detailed formal structures, explicit and implicit dependencies and rich semantic content. Given these document properties, contract drafting and manual examination of contracts have proven to be both arduous and susceptible to errors. This work aims to simplify and automate the task of contract review and analysis using a novel framework for transforming legal contracts into structured semantic graphs, enabling computational analysis and data-driven insights. We introduce a detailed ontology mapping core legal contract elements to their graph-theoretic equivalents of nodes and edges. We then present a reinforcement learning based Large Language Model (LLM) framework for segmentation and extraction of entities and relationships from contracts. Our method, GRAPH-GRPO-LEX, incorporates both LLMs and reinforcement learning with group relative policy optimization (GRPO). By applying a carefully drafted reward function of graph metrics, we demonstrate the ability to automatically identify direct relationships between clauses, and even uncover hidden dependencies. Our introduction of the gated GRPO approach shows a strong learning signal and can move contract analysis from a linear, manual reading process to an easily visualized graph. This allows for a more dynamic analysis, including building the groundwork for contract linting similar to what is now practiced in software engineering.
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