用大模型打造铁路服务智能助手，支持订票、推荐餐食和天气查询。
LLM4Rail: An LLM-Augmented Railway Service Consulting Platform
- 采用问答思考-行动-观察框架，结合推理与实际操作提升响应准确性。
- 构建中文铁路餐饮数据集CRFD-25，覆盖25类特色菜品及口味标签。
- 零样本对话推荐系统+特征相似度过滤，确保餐食推荐精准匹配用户需求。
大型语言模型显著改变了多个行业形态。为满足日益增长的个性化铁路服务需求，我们开发了LLM4Rail——一个基于大模型增强的铁路服务咨询平台。该平台支持票务、列车餐饮推荐、天气信息及闲聊等功能。核心提出迭代式‘问题-思考-行动-观察’（QTAO）提示框架，将语言推理与任务导向行为深度融合，通过推理指导动作选择，有效获取与铁路运营和服务相关的外部观测信息，生成准确回答。为实现个性化车内餐饮服务，我们构建了公开可用的中国铁路餐饮数据集CRFD-25，涵盖25类特色菜品，按城市、菜系、年龄群体和辣度分级。进一步提出基于大模型的零样本对话式餐饮推荐系统，并引入基于特征相似度的后处理步骤，确保所有推荐项均符合CRFD-25数据集规范。
原文摘要 · Abstract (English)
Large language models (LLMs) have significantly reshaped different walks of business. To meet the increasing demands for individualized railway service, we develop LLM4Rail - a novel LLM-augmented railway service consulting platform. Empowered by LLM, LLM4Rail can provide custom modules for ticketing, railway food & drink recommendations, weather information, and chitchat. In LLM4Rail, we propose the iterative "Question-Thought-Action-Observation (QTAO)" prompting framework. It meticulously integrates verbal reasoning with task-oriented actions, that is, reasoning to guide action selection, to effectively retrieve external observations relevant to railway operation and service to generate accurate responses. To provide personalized onboard dining services, we first construct the Chinese Railway Food and Drink (CRFD-25) - a publicly accessible takeout dataset tailored for railway services. CRFD-25 covers a wide range of signature dishes categorized by cities, cuisines, age groups, and spiciness levels. We further introduce an LLM-based zero-shot conversational recommender for railway catering. To address the unconstrained nature of open recommendations, the feature similarity-based post-processing step is introduced to ensure all the recommended items are aligned with CRFD-25 dataset.
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