Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations

• URL: http://arxiv.org/abs/2509.09651v1
• Date: Thu, 11 Sep 2025 17:43:42 GMT
• Title: Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
• Authors: Zakaria El Kassimi, Fares Fourati, Mohamed-Slim Alouini,
• Abstract summary: We study question answering in the domain of radio regulations.<n>We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline.<n>Our approach consistently improves generation accuracy across all tested models.
• Score: 49.671779378073886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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