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.

翻译：我们提出了一种利用大型语言模型（LLMs）实现法律文本自动化形式化的综合方法，旨在将其转化为可废止道义逻辑（DDL）。该方法采用结构化流程，将复杂的规范性语言分割为原子片段，提取道义规则，并评估其句法和语义连贯性。我们引入了一种更精确捕捉形式化完整性的改进成功率度量标准，以及一个包含专门优化步骤的新型两阶段流程，以提高逻辑一致性和覆盖范围。评估程序通过更严格的错误评估得到加强，并且我们提供了多种LLM配置的比较结果，包括新发布的模型以及不同的提示和微调策略。在澳大利亚电信消费者保护法规法律规范上的实验表明，在有效引导下，LLMs能够生成与专家构建的表征高度一致的形式化结果，凸显了其在可扩展法律信息学中的潜力。