用大模型将法律文本转为逻辑形式，提升自动化合规分析能力
Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs
- 分步处理法律条文，提取规则并验证逻辑一致性
- 新评估指标显示形式化完整率显著提升，优于传统方法
- 适合法律科技、合规系统开发者参考
本文提出一种基于大语言模型（LLMs）的法律文本自动化形式化方法，目标是将其转化为可计算的非确定性义务逻辑（Defeasible Deontic Logic, DDL）。方法采用结构化流水线，将复杂规范语言拆分为原子片段，提取义务规则，并评估其语法与语义一致性。引入更精准的成功度量以衡量形式化完整性，设计包含专门优化阶段的两阶段流程，增强逻辑一致性和覆盖范围。评估流程经强化，采用更严格的错误判定标准，并对比多种LLM配置，包括新发布模型及不同提示工程与微调策略。在澳大利亚电信消费者保护法（Australian Telecommunications Consumer Protections Code）的法律规范上实验表明，经有效引导后，大模型生成的形式化结果能高度贴近专家手写版本，凸显其在可扩展法律信息学中的潜力。
原文摘要 · Abstract (English)
We present a comprehensive approach to the automated formalization of legal texts using large language models (LLMs), targeting their transformation into Defeasible Deontic Logic (DDL). Our method employs a structured pipeline that segments complex normative language into atomic snippets, extracts deontic rules, and evaluates them for syntactic and semantic coherence. We introduce a refined success metric that more precisely captures the completeness of formalizations, and a novel two-stage pipeline with a dedicated refinement step to improve logical consistency and coverage. The evaluation procedure has been strengthened with stricter error assessment, and we provide comparative results across multiple LLM configurations, including newly released models and various prompting and fine-tuning strategies. Experiments on legal norms from the Australian Telecommunications Consumer Protections Code demonstrate that, when guided effectively, LLMs can produce formalizations that align closely with expert-crafted representations, underscoring their potential for scalable legal informatics.
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