Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911345903927296 |
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| author | Horner, Elias Mateis, Cristinel Governatori, Guido Ciabattoni, Agata |
| author_facet | Horner, Elias Mateis, Cristinel Governatori, Guido Ciabattoni, Agata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs Horner, Elias Mateis, Cristinel Governatori, Guido Ciabattoni, Agata Computation and Language Artificial Intelligence Computers and Society Logic in Computer Science 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. |
| title | Toward Robust Legal Text Formalization into Defeasible Deontic Logic using LLMs |
| topic | Computation and Language Artificial Intelligence Computers and Society Logic in Computer Science |
| url | https://arxiv.org/abs/2506.08899 |