迈向使用LLMs的稳健法律文本形式化到可撤销的规范逻辑
Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs
AI总结：
本文提出利用LLMs将法律文本形式化为可撤销规范逻辑，通过结构化管道和改进的评估方法，提升形式化的一致性和完整性。
摘要：
我们提出了一种全面的方法，利用大型语言模型（LLMs）自动形式化法律文本，目标是将其转换为可撤销的规范逻辑（DDL）。我们的方法采用了一种结构化的管道，将复杂的规范语言分割成原子片段，提取规范规则，并评估其语法和语义一致性。我们引入了一个改进的成功度量标准，更精确地捕捉形式化的完整性，以及一种新的两阶段管道，包含专门的细化步骤以提高逻辑一致性和覆盖范围。评估程序已通过更严格的错误评估加强，我们提供了在多种LLM配置下的比较结果，包括新发布的模型和各种提示和微调策略。在澳大利亚电信消费者保护代码的法律规范上的实验表明，当有效引导时，LLMs可以生成与专家编写的表示紧密契合的形式化，凸显了其在可扩展法律信息学中的潜力。
英文摘要：
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.
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。
