TelcoAI: 通过代理多模态检索增强生成技术推进3GPP技术规范搜索
TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation
AI总结：
TelcoAI通过代理多模态检索增强生成技术，提升3GPP技术规范搜索的准确性和效率，实现87%的召回率和16%的性能提升。
摘要：
第三世代合作伙伴计划（3GPP）产生复杂的技术规范，对全球电信至关重要，但其层级结构、密集格式和多模态内容使其难以处理。尽管大型语言模型（LLMs）显示出潜力，但现有方法在处理复杂查询、视觉信息和文档依赖性方面存在不足。我们提出了TelcoAI，一种专门针对3GPP文档的代理多模态检索增强生成（RAG）系统。TelcoAI引入了面向章节的分块、结构化查询规划、元数据引导检索以及文本和图表的多模态融合。在多个基准测试中，包括专家精心编写的查询，我们的系统实现了87%的召回率、83%的声明召回率和92%的忠实度，比最先进的基线提高了16%。这些结果证明了代理和多模态推理在技术文档理解中的有效性，为现实世界电信研究和工程提供了实际解决方案。
英文摘要：
The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Language Models (LLMs) show promise, existing approaches fall short in handling complex queries, visual information, and document interdependencies. We present TelcoAI, an agentic, multi-modal Retrieval-Augmented Generation (RAG) system tailored for 3GPP documentation. TelcoAI introduces section-aware chunking, structured query planning, metadata-guided retrieval, and multi-modal fusion of text and diagrams. Evaluated on multiple benchmarks-including expert-curated queries-our system achieves $87\%$ recall, $83\%$ claim recall, and $92\%$ faithfulness, representing a $16\%$ improvement over state-of-the-art baselines. These results demonstrate the effectiveness of agentic and multi-modal reasoning in technical document understanding, advancing practical solutions for real-world telecommunications research and engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
