LLM4Rail: An LLM-Augmented Railway Service Consulting Platform
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911085434503168 |
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| author | Li, Zhuo Deng, Xianghuai Feng, Chiwei Li, Hanmeng Wang, Shenjie Zhang, Haichao Jia, Teng Chen, Conlin Wu, Louis Linchun Wang, Jia |
| author_facet | Li, Zhuo Deng, Xianghuai Feng, Chiwei Li, Hanmeng Wang, Shenjie Zhang, Haichao Jia, Teng Chen, Conlin Wu, Louis Linchun Wang, Jia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4Rail: An LLM-Augmented Railway Service Consulting Platform Li, Zhuo Deng, Xianghuai Feng, Chiwei Li, Hanmeng Wang, Shenjie Zhang, Haichao Jia, Teng Chen, Conlin Wu, Louis Linchun Wang, Jia Artificial Intelligence 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. |
| title | LLM4Rail: An LLM-Augmented Railway Service Consulting Platform |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.23377 |