Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps

Khandakar Ashrafi Akbar; Md Nahiyan Uddin; Latifur Khan; Trayce Hockstad; Mizanur Rahman; Mashrur Chowdhury; Bhavani Thuraisingham

arXiv · 2505.18426

Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps

Khandakar Ashrafi Akbar·Md Nahiyan Uddin·Latifur Khan·Trayce Hockstad·Mizanur Rahman·Mashrur Chowdhury·Bhavani Thuraisingham

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Abstract

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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Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan, Trayce Hockstad, Mizanur Rahman, Mashrur Chowdhury, Bhavani Thuraisingham. 2025-05-23. Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps. https://arxiv.org/abs/2505.18426

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