🤖 AI Summary
This study addresses the challenges of legal metric computation—namely textual complexity, large scale, high interpretability demands, and variable data quality—which often lead existing methods to produce hallucinations and lack explainability or evidentiary support. To overcome these limitations, this work proposes the first agent-driven Retrieval-Augmented Generation (RAG) framework tailored for legal metric calculation. The approach employs a modular pipeline that integrates adaptive retrieval, large language model agents, and validation mechanisms to enable transparent and traceable evidence selection, statutory linkage, and binary judgment. Experiments on a newly constructed corpus of French maritime environmental law demonstrate that the proposed method significantly outperforms baseline systems and exhibits strong generalization across two injunction-related tasks, offering a novel paradigm for building trustworthy and scalable legal monitoring systems.
📝 Abstract
Computing legal indicators from normative texts is a key task in legal monitoring and policy evaluation, but presents significant challenges due to the complexity, scale, and interpretive nature of legal language, as well as the variability in available document quality. Existing natural language processing techniques and generative models can assist in legal analysis, but often suffer from high risk of hallucinations and lack the interpretability and evidence grounding required for reliable indicator computation. This paper presents N2I-RAG (From Norms to Indicators), an agentic retrieval-augmented generation framework designed to automate the computation of legal indicators in a transparent and traceable way. We integrate adaptive retrieval, llm-based agents, and validation mechanisms in a modular pipeline, where each component performs a defined role in filtering, retrieving, and assessing evidence, and in producing binary legal outcomes linked to identifiable legal provisions. The framework emphasizes traceability by requiring explicit explanations of intermediate decisions and final indicator assignments. We evaluate N2I-RAG using an in-house constructed French marine environmental law corpus that includes both scanned and digital sources. Comparative experiments with multiple language model families demonstrate that the proposed approach consistently outperforms baseline systems, and generalizes well when tested on 2 different bans. The results indicate that agentic retrieval-augmented generation can bridge open-text legal language and standardized indicator computation, offering a foundation for transparent and scalable legal observatories.