ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulation

TL;DR

This paper presents ASVRI-Legal, a method that combines fine-tuning of LLMs with retrieval-augmented generation to improve legal regulation understanding and drafting, aiding policymakers with up-to-date legal insights.

Contribution

It introduces a novel approach integrating fine-tuning and RAG specifically tailored for legal regulation tasks, enhancing LLM capabilities in the legal domain.

Findings

Improved legal text comprehension and analysis.

Enhanced support for policymakers in regulation drafting.

Significant performance gains over baseline models.

Abstract

In this study, we explore the fine-tuning of Large Language Models (LLMs) to better support policymakers in their crucial work of understanding, analyzing, and crafting legal regulations. To equip the model with a deep understanding of legal texts, we curated a supervised dataset tailored to the specific needs of the legal domain. Additionally, we integrated the Retrieval-Augmented Generation (RAG) method, enabling the LLM to access and incorporate up-to-date legal knowledge from external sources. This combination of fine-tuning and RAG-based augmentation results in a tool that not only processes legal information but actively assists policymakers in interpreting regulations and drafting new ones that align with current needs. The results demonstrate that this approach can significantly enhance the effectiveness of legal research and regulation development, offering a valuable resource in the ever-evolving field of law.