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

2024-08-29

CUSTOMNLP4U

AI Summary

To address knowledge obsolescence, frequent hallucinations, and insufficient complex reasoning in large language models (LLMs) for AI-powered legal applications, this paper proposes a highly adaptive retrieval-augmented generation (RAG) framework, empirically grounded in New York City’s Local Law 144 (the Algorithmic Accountability Act). The method introduces a query-complexity-driven parameter adaptation mechanism; integrates multi-source hybrid retrieval—combining dense retrieval, sparse retrieval, and a domain-specific legal knowledge graph; and establishes a faithfulness-aware evaluation framework tailored to legal texts. Experiments demonstrate significant improvements in retrieval accuracy, answer correctness, factual faithfulness, and context precision, alongside reduced hallucination rates and reasoning bias. These results validate the framework’s effectiveness and reliability in high-stakes legal decision-making scenarios.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

While Large Language Models (LLMs) excel in text generation and question-answering, their effectiveness in AI legal and policy applications is limited by outdated knowledge, hallucinations, and inadequate reasoning in complex contexts. Retrieval-Augmented Generation (RAG) systems improve response accuracy by integrating external knowledge but struggle with retrieval errors, poor context integration, and high costs, particularly in interpreting AI legal texts. This paper introduces a Hybrid Parameter-Adaptive RAG (HyPA-RAG) system tailored for AI legal and policy, exemplified by NYC Local Law 144 (LL144). HyPA-RAG uses a query complexity classifier for adaptive parameter tuning, a hybrid retrieval strategy combining dense, sparse, and knowledge graph methods, and an evaluation framework with specific question types and metrics. By dynamically adjusting parameters, HyPA-RAG significantly improves retrieval accuracy and response fidelity. Testing on LL144 shows enhanced correctness, faithfulness, and contextual precision, addressing the need for adaptable NLP systems in complex, high-stakes AI legal and policy applications.

Problem

• Improves retrieval accuracy in legal AI
• Enhances response fidelity in policy applications
• Increases contextual precision in complex scenarios

Innovation

• Hybrid retrieval with multiple methods
• Adaptive parameter tuning system
• Comprehensive evaluation framework