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Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
Created by
Haebom
Author
Zakaria El Kassimi, Fares Fourati, Mohamed-Slim Alouini
Outline
This paper studies question answering in the legally sensitive and critical domain of radio regulation. We propose a telecommunications-specific search-augmented generation (RAG) pipeline and present the first multiple-choice evaluation set for this domain, constructed from authoritative sources using automated filtering and human verification. We define a domain-specific retrieval metric to evaluate retrieval quality and demonstrate that the retrieval system achieves approximately 97% accuracy under this metric. Beyond retrieval, the proposed approach consistently improves generation accuracy across all tested models. Notably, while simply embedding documents without structured retrieval yields only a marginal gain (less than 1%) for GPT-4o, applying the proposed pipeline yields a relative improvement of nearly 12%. These results demonstrate that carefully targeted evidence provides a simple yet powerful standard and an effective domain-specific solution for regulatory question answering. All code, evaluation scripts, and the derived question-answering dataset are available at https://github.com/Zakaria010/Radio-RAG .
