用检索增强生成技术提升无线电法规问答准确率
Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
- 构建电信领域专用RAG流程，结合自动筛选与人工验证构造首个多选题数据集
- 检索模块准确率达97%，使GPT-4o生成准确率相对提升近12%
- 适合法律、通信监管等高要求领域的可信赖问答系统研发者
我们研究无线电法规领域的问答任务，该领域具有法律敏感性和高风险性。提出一种面向电信领域的检索增强生成（RAG）流程，并首次构建了基于权威来源、经自动化过滤与人工验证的多选题评估集。为评估检索质量，设计领域特定的检索指标，结果显示该检索器准确率约97%。在生成环节，该方法在所有测试模型上均提升准确率；仅将文档无结构插入GPT-4o时，准确率提升不足1%，而采用本流程后，相对提升接近12%。结果表明，精准定位的外部知识注入是监管问答任务中简单而强大的基线方案。所有代码、评估脚本及衍生数据集已开源至https://github.com/Zakaria010/Radio-RAG。
原文摘要 · Abstract (English)
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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