HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications

TL;DR

HyPA-RAG is a novel system that combines adaptive parameter tuning and hybrid retrieval methods to improve the accuracy and reliability of AI models in legal and policy contexts, addressing key limitations of existing LLM-based systems.

Contribution

The paper introduces HyPA-RAG, a hybrid retrieval-augmented generation system with adaptive parameters tailored for legal applications, demonstrating improved performance over existing methods.

Findings

Enhanced retrieval accuracy in legal domain

Improved response fidelity and contextual understanding

Effective handling of complex legal queries

Abstract

Large Language Models (LLMs) face limitations in AI legal and policy applications due to outdated knowledge, hallucinations, and poor reasoning in complex contexts. Retrieval-Augmented Generation (RAG) systems address these issues by incorporating external knowledge, but suffer from retrieval errors, ineffective context integration, and high operational costs. This paper presents the Hybrid Parameter-Adaptive RAG (HyPA-RAG) system, designed for the AI legal domain, with NYC Local Law 144 (LL144) as the test case. HyPA-RAG integrates a query complexity classifier for adaptive parameter tuning, a hybrid retrieval approach combining dense, sparse, and knowledge graph methods, and a comprehensive evaluation framework with tailored question types and metrics. Testing on LL144 demonstrates that HyPA-RAG enhances retrieval accuracy, response fidelity, and contextual precision, offering a robust and adaptable solution for high-stakes legal and policy applications.