LLM4Rail: An LLM-Augmented Railway Service Consulting Platform

Zhuo Li; Xianghuai Deng; Chiwei Feng; Hanmeng Li; Shenjie Wang; Haichao Zhang; Teng Jia; Conlin Chen; Louis Linchun Wu; Jia Wang

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arXiv · 2507.23377

LLM4Rail: An LLM-Augmented Railway Service Consulting Platform

Zhuo Li·Xianghuai Deng·Chiwei Feng·Hanmeng Li·Shenjie Wang·Haichao Zhang·Teng Jia·Conlin Chen·Louis Linchun Wu·Jia Wang

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Abstract

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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Zhuo Li, Xianghuai Deng, Chiwei Feng, Hanmeng Li, Shenjie Wang, Haichao Zhang, Teng Jia, Conlin Chen, Louis Linchun Wu, Jia Wang. 2025-07-31. LLM4Rail: An LLM-Augmented Railway Service Consulting Platform. https://arxiv.org/abs/2507.23377

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