De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules
Regulatory documents encode legally binding obligations that LLM-based systems must respect. Yet converting dense, hierarchically structured legal text into machine-readable rules remains a costly, expert-intensive process. We present De Jure, a fully automated, domain-agnostic pipeline for extracting structured regulatory rules from raw documents, requiring no human annotation, domain-specific prompting, or annotated gold data. De Jure operates through four sequential stages: normalization of source documents into structured Markdown; LLM-driven semantic decomposition into structured rule units; multi-criteria LLM-as-a-judge evaluation across 19 dimensions spanning metadata, definitions,...
| Authors | Keerat Guliani, Deepkamal Gill, David Landsman et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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