Law RAG: Retrieval-Augmented Generation for Judicial Case Law: An Embedding Model Benchmark
Year: 2025
Type: article
Abstract: This paper presents LawRAG, an advanced Retrieval-Augmented Generation (RAG) system designed for legal question answering using judicial case law in the Australian legal domain. The framework integrates legal document corpora, optimized vector embeddings, and state-of-the-art large language modelto produce authoritative, contextually grounded responses. Unlike prior work focused on statutory texts, LawRAG addresses the nuanced structure of court judgments through an innovative parent document retrieval strategy. This method preserves critical legal context and improves factual accuracy. We evaluate multiple embedding models on a rigorously curated legal QA dataset, identifying GTE-large as the most reliable encoder, achieving a BERT Score of 0.8476 and the highest answer relevancy (0.7444). The system’s Dockerized implementation offers a fully reproducible pipeline for judicial case law analysis, establishing new best practices for contextual retrieval in legal AI applications
Author: Tuijin Jishu/Journal of Propulsion Technology
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893335735
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