Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Zhuo Li, , ,
| Summary:
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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