专为电信领域优化的智能问答框架，提升3GPP问题解答准确率。
Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routing
- 融合专有检索与网络搜索，通过术语增强查询改写
- 3GPP相关问题准确率提升17.6%，词表查询提升10.6%
- 内存占用降低45%，开源大模型达GPT-4级表现
人工智能将成为下一代移动网络（6G）的核心支柱，有望提供新型增值服务并提升网络性能。在此背景下，大语言模型具备通过意图理解、智能知识检索、编程能力及跨域协同革新电信行业的潜力。本文提出 Telco-oRAG，一个开源的检索增强生成（RAG）框架，专为回答电信领域技术问题优化，尤其聚焦3GPP标准。该框架引入混合检索策略，结合3GPP领域专用检索与网络搜索，辅以术语增强的查询改写和神经路由机制实现内存高效检索。实验结果表明，Telco-oRAG在回答3GPP相关问题上准确率提升最高达17.6%，词表查询准确率提升10.6%；同时通过针对性检索相关3GPP系列，内存使用减少45%；使开源大模型在电信基准测试中达到GPT-4水平的准确率。
原文摘要 · Abstract (English)
Artificial intelligence will be one of the key pillars of the next generation of mobile networks (6G), as it is expected to provide novel added-value services and improve network performance. In this context, large language models have the potential to revolutionize the telecom landscape through intent comprehension, intelligent knowledge retrieval, coding proficiency, and cross-domain orchestration capabilities. This paper presents Telco-oRAG, an open-source Retrieval-Augmented Generation (RAG) framework optimized for answering technical questions in the telecommunications domain, with a particular focus on 3GPP standards. Telco-oRAG introduces a hybrid retrieval strategy that combines 3GPP domain-specific retrieval with web search, supported by glossary-enhanced query refinement and a neural router for memory-efficient retrieval. Our results show that Telco-oRAG improves the accuracy in answering 3GPP-related questions by up to 17.6% and achieves a 10.6% improvement in lexicon queries compared to baselines. Furthermore, Telco-oRAG reduces memory usage by 45% through targeted retrieval of relevant 3GPP series compared to baseline RAG, and enables open-source LLMs to reach GPT-4-level accuracy on telecom benchmarks.
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