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AI Research
arXiv CS.AI·May 27·8 min read
From Norms to Indicators (N2I-RAG): An Agentic Retrieval-Augmented Generation Framework for Legal Indicator Computation
TL;DR
Legal analysts struggle to extract measurable indicators from complex normative texts due to hallucinations and poor interpretability in existing NLP models. N2I-RAG (Norms to Indicators Retrieval-Augmented Generation) is an agentic framework that grounds legal analysis in source documents to compute reliable indicators.
✦ Why It Matters
Engineers can build more trustworthy legal AI systems by adopting evidence-grounded agentic RAG patterns instead of relying on unanchored generative models.
Key Takeaways
- Utilize N2I-RAG for automated legal indicator computation.
- Ensure transparency in legal decision-making processes.
- Leverage adaptive retrieval for improved evidence assessment.
- Test the framework on diverse legal texts for robust results.
How It Works
N2I-RAG integrates adaptive retrieval mechanisms with LLM-based agents to filter and assess legal documents. Each component in the modular pipeline plays a specific role, ensuring that the evidence used for legal outcomes is both relevant and traceable.
The framework requires explicit explanations for each decision, enhancing interpretability and accountability.