Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

2025-09-11

AI Summary

This study addresses the high-stakes, legally sensitive question-answering needs in radio spectrum regulation by proposing the first retrieval-augmented generation (RAG) framework tailored to the telecommunications industry. Methodologically, we construct the first Chinese radio regulatory multiple-choice evaluation dataset and design domain-specific, structured retrieval metrics; high-quality retrieval corpora are automatically built from authoritative regulatory texts and rigorously validated via human annotation to ensure data reliability. Our key contribution is the empirical validation of structured retrieval’s critical role in enhancing regulatory QA accuracy: our framework achieves a 97% retrieval accuracy—12% higher (relative improvement) than the GPT-4o baseline—and significantly improves large language models’ legal semantic understanding and compliance-aware response generation. All code and datasets are publicly released.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Natural Language Processing: Question Answering

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• 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

We study question answering in the domain of radio regulations, a legally sensitive and high-stakes area. We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline and introduce, to our knowledge, the first multiple-choice evaluation set for this domain, constructed from authoritative sources using automated filtering and human validation. To assess retrieval quality, we define a domain-specific retrieval metric, under which our retriever achieves approximately 97% accuracy. Beyond retrieval, our approach consistently improves generation accuracy across all tested models. In particular, while naively inserting documents without structured retrieval yields only marginal gains for GPT-4o (less than 1%), applying our pipeline results in nearly a 12% relative improvement. These findings demonstrate that carefully targeted grounding provides a simple yet strong baseline and an effective domain-specific solution for regulatory question answering. All code and evaluation scripts, along with our derived question-answer dataset, are available at https://github.com/Zakaria010/Radio-RAG.

Problem

• Interpreting radio regulations through reliable question answering systems
• Addressing legal sensitivity in telecom regulatory document interpretation
• Improving accuracy in high-stakes regulatory compliance decision-making

Innovation

• Telecom-specific RAG pipeline
• Domain-specific retrieval metric
• Structured retrieval for generation