用检索增强生成技术帮政策制定者精准回答交通网络安全法律问题。
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
- 用领域特定问题引导，结合检索机制减少大模型幻觉。
- 在四个指标上优于主流商业大模型，输出更准确可靠。
- 适合政策研究、法律科技和智能立法系统开发者参考。
随着车联网与自动驾驶系统的发展，联邦及州级机构亟需修订现有法规并制定新法以应对新兴的网络安全与数据隐私挑战。本研究提出一种基于检索增强生成（RAG）的大语言模型框架，旨在支持政策制定者提取相关法律内容并生成针对性回答。该框架通过使用经过筛选的领域特定问题引导响应生成，有效降低大模型幻觉。结合检索机制，提升了输出的事实性与上下文相关性。分析表明，所提出的RAG-LMM在AlignScore、ParaScore、BERTScore和ROUGE四项评估指标上均优于领先商业大模型，证明其在生成可靠且情境感知的法律洞见方面具有显著优势。该方法为立法分析提供了一种可扩展的AI驱动方案，助力法律框架随交通技术进步及时更新。
原文摘要 · Abstract (English)
As connected and automated transportation systems evolve, there is a growing need for federal and state authorities to revise existing laws and develop new statutes to address emerging cybersecurity and data privacy challenges. This study introduces a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) framework designed to support policymakers by extracting relevant legal content and generating accurate, inquiry-specific responses. The framework focuses on reducing hallucinations in LLMs by using a curated set of domain-specific questions to guide response generation. By incorporating retrieval mechanisms, the system enhances the factual grounding and specificity of its outputs. Our analysis shows that the proposed RAG-based LLM outperforms leading commercial LLMs across four evaluation metrics: AlignScore, ParaScore, BERTScore, and ROUGE, demonstrating its effectiveness in producing reliable and context-aware legal insights. This approach offers a scalable, AI-driven method for legislative analysis, supporting efforts to update legal frameworks in line with advancements in transportation technologies.
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