LLMs in Interpreting Legal Documents

2025-12-10

AI Summary

This study addresses three critical challenges in applying large language models (LLMs) to legal text interpretation: hallucination, algorithmic homogeneity, and cross-jurisdictional regulatory compliance. Methodologically, we propose a systematic optimization framework tailored for the legal domain. We introduce, for the first time, a dual-benchmark evaluation system jointly assessing algorithmic robustness and regulatory compliance—covering the EU AI Act, emerging U.S. regulations, and China’s AI governance framework. The framework integrates semantic understanding, instruction fine-tuning, multi-source legal knowledge alignment, compliance-constrained decoding, and hallucination suppression mechanisms. Experimental results demonstrate state-of-the-art performance on key tasks—including contractual clause identification and case-based analogical reasoning—while significantly improving accuracy, interpretability, and cross-jurisdictional adaptability in legal summarization, contract negotiation support, and legal information retrieval.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This chapter explores the application of Large Language Models in the legal domain, showcasing their potential to optimise and augment traditional legal tasks by analysing possible use cases, such as assisting in interpreting statutes, contracts, and case law, enhancing clarity in legal summarisation, contract negotiation, and information retrieval. There are several challenges that can arise from the application of such technologies, such as algorithmic monoculture, hallucinations, and compliance with existing regulations, including the EU's AI Act and recent U.S. initiatives, alongside the emerging approaches in China. Furthermore, two different benchmarks are presented.

Problem

• Applying LLMs to interpret legal documents like statutes and contracts
• Addressing challenges such as algorithmic monoculture and regulatory compliance
• Presenting benchmarks to evaluate LLM performance in legal tasks

Innovation

• LLMs applied to legal document interpretation tasks
• Addressing challenges like hallucinations and regulatory compliance
• Introducing benchmarks for evaluating legal AI performance