Can Legislation Be Made Machine-Readable in PROLEG?

2026-01-04

arXiv.org

AI Summary

This work addresses the critical challenge of efficiently and accurately translating natural language legal provisions into machine-executable logic programs for intelligent regulatory compliance. The authors propose an end-to-end framework that, for the first time, leverages a single large language model (LLM) prompt to simultaneously generate human-readable if-then rules and their corresponding formal encodings in PROLEG. The approach incorporates a closed-loop refinement mechanism guided by legal expert feedback to enhance accuracy and fidelity. Demonstrated on Article 6 of the GDPR, the method successfully produces executable logic programs that support precise legal reasoning while also generating interpretable, human-readable explanations of regulatory decisions. This study validates the feasibility and practical utility of an automated pipeline for transforming unstructured legal texts into executable logical representations.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Logic Programming
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The anticipated positive social impact of regulatory processes requires both the accuracy and efficiency of their application. Modern artificial intelligence technologies, including natural language processing and machine-assisted reasoning, hold great promise for addressing this challenge. We present a framework to address the challenge of tools for regulatory application, based on current state-of-the-art (SOTA) methods for natural language processing (large language models or LLMs) and formalization of legal reasoning (the legal representation system PROLEG). As an example, we focus on Article 6 of the European General Data Protection Regulation (GDPR). In our framework, a single LLM prompt simultaneously transforms legal text into if-then rules and a corresponding PROLEG encoding, which are then validated and refined by legal domain experts. The final output is an executable PROLEG program that can produce human-readable explanations for instances of GDPR decisions. We describe processes to support the end-to-end transformation of a segment of a regulatory document (Article 6 from GDPR), including the prompting frame to guide an LLM to"compile"natural language text to if-then rules, then to further"compile"the vetted if-then rules to PROLEG. Finally, we produce an instance that shows the PROLEG execution. We conclude by summarizing the value of this approach and note observed limitations with suggestions to further develop such technologies for capturing and deploying regulatory frameworks.

Problem

• machine-readable legislation
• regulatory compliance
• legal formalization
• GDPR
• executable law

Innovation

• machine-readable legislation
• large language models
• legal reasoning
• PROLEG
• rule extraction