摘要
随着铁路旅客服务需求的持续增长,传统信息查询系统因理解能力有限、上下文感知不足等问题,难以满足乘客与调度人员对高效、准确信息服务的需求。针对这一问题,提出一种融合大语言模型(Large Language Models,LLMs)与检索增强生成(Retrieval-Augmented Generation,RAG)技术的智能问答系统,专注于铁路旅客服务场景下的信息问答任务。该系统通过RAG机制融合语义检索与语言生成,提升对铁路专业知识的响应能力和答案准确性,同时构建了覆盖行业规则和乘客服务内容的知识库。基于500条真实问题设计实验,对比关键词匹配与无检索生成方法。结果表明,本系统在500条真实问题测试中取得了91.2%的回答准确率,较关键词检索方法提升近19%,较ChatGLM提升约10%;平均响应时间为3.2 s,用户满意度评分达到4.6/5.0,各项指标均显著优于对比系统,验证了本方法在铁路旅客服务场景中的实用性与推广价值。
With the continuous growth of passenger service demands in the railway sector,traditional information query systems struggle to meet the needs for efficient and accurate information services for passengers and dispatchers,due to limitations in comprehension and context awareness.To address this issue,the paper proposed an intelligent question-answering(Q&A)system that integrates Large Language Models(LLMs)with Retrieval-augmented Generation(RAG)technology,specifically tailored for railway passenger service scenarios.The system leveraged the RAG framework to combine semantic retrieval with language generation,to enhance its capability to respond to domain-specific railway knowledge and improve answer accuracy,while also constructed a knowledge base covering industry regulations and passenger service content.Experiments were designed based on 500 real-world user queries,comparing the system with keyword matching and non-retrieval generation methods.The results show that the proposed system achieves an answer accuracy of 91.2%on the 500-question test set,outperforming keyword retrieval methods by nearly 19%and surpassing ChatGLM by approximately 10%.The system also achieves an average response time of 3.2 seconds and user satisfaction score of 4.6/5.0.These results validate the practicality and potential for broader application of the proposed method in railway passenger service scenarios.
作者
张中华
胡金先
刘宇哲
ZHANG Zhonghua;HU Jinxian;LIU Yuzhe(China Railway Urumqi Group Co.,Ltd.,Urumqi 830011,China)
出处
《高速铁路技术》
2025年第6期126-132,共7页
High Speed Railway Technology
基金
中国国家铁路集团有限公司科技研究开发计划(P2024S001)。
关键词
RAG技术
大语言模型
智能问答
铁路旅客服务
信息检索
Retrieval-augmented Generation(RAG)
Large Language Models(LLMs)
intelligent Q&A system
railway passenger service
information retrieval
