Automating Legal Concept Interpretation with LLMs: Retrieval, Generation, and Evaluation

2025-01-03

AI Summary

Legal concepts in statutes evolve dynamically with societal changes, rendering manual interpretation time-consuming, labor-intensive, and heavily reliant on scarce domain expertise. Method: This paper proposes ATRI, a retrieval-augmented generation framework that retrieves semantically relevant judicial precedents from case law repositories to support precise concept grounding, and leverages LLM-based prompt engineering to generate professional-grade explanations. Contribution/Results: We introduce the first automatic evaluation benchmark for Legal Concept Entailment (LCE) and develop a self-supervised assessment model. Experiments demonstrate that ATRI significantly improves LLMs’ comprehension of ambiguous legal concepts across multiple automated metrics. Moreover, blind multidimensional evaluations by legal experts confirm that ATRI’s outputs achieve human-expert-level quality—establishing a verifiable, deployable paradigm for legal concept explanation in AI systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Generation
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Legal articles often include vague concepts to adapt to the ever-changing society. Providing detailed interpretations of these concepts is a critical task for legal practitioners, which requires meticulous and professional annotations by legal experts, admittedly time-consuming and expensive to collect at scale. In this paper, we introduce a novel retrieval-augmented generation framework, ATRI, for AuTomatically Retrieving relevant information from past judicial precedents and Interpreting vague legal concepts. We further propose a new benchmark, Legal Concept Entailment, to automate the evaluation of generated concept interpretations without expert involvement. Automatic evaluations indicate that our generated interpretations can effectively assist large language models (LLMs) in understanding vague legal concepts. Multi-faceted evaluations by legal experts indicate that the quality of our concept interpretations is comparable to those written by human experts. Our work has strong implications for leveraging LLMs to support legal practitioners in interpreting vague legal concepts and beyond.

Problem

• Legal Terminology
• Social Change
• Expert Resource Limitations

Innovation

• ATRI
• Automatic Explanation Generation
• Legal Concept Understanding