针对法律AI知识过时与幻觉问题，提出自适应混合检索生成系统。
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
- 根据查询复杂度动态调整参数，提升系统响应精准度。
- 融合稠密、稀疏与知识图谱检索，召回率显著提升。
- 专为高风险法律政策场景设计，适合司法与合规领域应用。
大型语言模型在人工智能法律与政策应用中受限于知识陈旧、幻觉及复杂情境下的推理能力不足。检索增强生成（RAG）系统通过引入外部知识缓解这些问题，但存在检索错误、上下文整合低效和运行成本高等挑战。本文提出混合参数自适应检索增强生成（HyPA-RAG）系统，面向法律领域，以纽约市本地法144号（LL144）为测试案例。该系统包含查询复杂度分类器以实现参数自适应调优，结合稠密、稀疏与知识图谱的混合检索策略，并构建了涵盖定制化问题类型与评估指标的综合评测框架。在LL144上的测试表明，HyPA-RAG显著提升了检索准确率、回答真实性和上下文精确度，为高风险法律与政策应用提供了鲁棒且可扩展的解决方案。
原文摘要 · Abstract (English)
Large Language Models (LLMs) face limitations in AI legal and policy applications due to outdated knowledge, hallucinations, and poor reasoning in complex contexts.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.
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