用大模型整合多格式数据，让维修人员更快更准获得指导。
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance
- 用大模型+检索增强生成，跨格式融合信息
- GPT-4等模型在复杂查询上准确率和速度显著领先
- 适合需要实时多源信息支持的工业维护场景
本文对一个融合大语言模型（LLMs）的检索增强生成（RAG）系统进行了详细评估，旨在提升不同数据格式下维修人员的信息检索与指令生成能力。我们测试了八种LLMs，重点考察响应速度与准确性，采用BLEU和METEOR分数量化评估。结果表明，GPT-4和GPT-4o-mini等先进模型在需跨格式数据整合的复杂查询中表现显著优于其他模型。系统能提供及时且准确的响应，验证了RAG框架在优化维护作业中的潜力。未来研究将聚焦于改进检索技术与复杂场景下的响应生成，以提升系统在动态真实环境中的实用性。
原文摘要 · Abstract (English)
This paper presents a detailed evaluation of a Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) to enhance information retrieval and instruction generation for maintenance personnel across diverse data formats. We assessed the performance of eight LLMs, emphasizing key metrics such as response speed and accuracy, which were quantified using BLEU and METEOR scores. Our findings reveal that advanced models like GPT-4 and GPT-4o-mini significantly outperform their counterparts, particularly when addressing complex queries requiring multi-format data integration. The results validate the system's ability to deliver timely and accurate responses, highlighting the potential of RAG frameworks to optimize maintenance operations. Future research will focus on refining retrieval techniques for these models and enhancing response generation, particularly for intricate scenarios, ultimately improving the system's practical applicability in dynamic real-world environments.
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