Construction and Evaluation of Large Language Models for the Cultural Tourism Domain via LoRA-Efficient Fine-Tuning

Ming Ge, Caihong Li, Yi Yang (), Zhiwen Chen, Xiangquan Gui and Fangfang Yu
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Ming Ge: Lanzhou University, School of Information Science and Engineering
Caihong Li: Ministry of Culture and Tourism, Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection
Yi Yang: Key Laboratory of Artificial Intelligence and Computing Power Technology (Gansu Province)
Zhiwen Chen: Lanzhou University, School of Information Science and Engineering
Xiangquan Gui: Data Intelligence Laboratory of Tibetan Plateau Humanistic Environment
Fangfang Yu: School of Foreign Languages, Lanzhou University of Arts and Science

Chapter 4 in Information and Communication Technologies in Tourism 2026, 2026, pp 45-58 from Springer

Abstract: Abstract The cultural and tourism industry plays a crucial role in the digital economy, driving economic growth and fostering cultural exchange. However, existing intelligent methods predominantly rely on general-purpose models, which often lack domain-specific adaptation. To address this, this study explores the feasibility of applying large language models in vertical domains, with a focus on Dali, Yunnan, as the experimental site. Scenic spot data is collected from the Ctrip platform using web crawlers, and a fine-tuning dataset is constructed through automated Question and Answer (Q&A) generation. The DeepSeek-R1-Distill-Qwen-1.5B model is adopted as the base model. Using Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning method and the LLaMA-Factory framework, the model is trained on 3A and above scenic spots, high-rated attractions, and representative travel routes. The fine-tuned model is then evaluated on tasks including self-awareness, domain-specific Q&A, and itinerary planning. The results indicate a reduction in training loss from 3.7 to 0.2444 in the initial phase, stabilizing at 0.0125 after convergence. In professional capability assessment, after 200 training epochs, the fine-tuned model demonstrates significant improvements in three aspects: self-awareness, scenic spot Q&A, and travel itinerary planning. It accurately interprets user queries and provides correct, expectation-aligned information, showing clear advantages over baseline models. This study offers a replicable approach for integrating AI into the cultural tourism sector, lowering the barrier to building intelligent industry solutions.

Keywords: Large language model; Fine-tuning; Cultural tourism; Domain adaptation; Low-rank adaptation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-23925-9_4

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DOI: 10.1007/978-3-032-23925-9_4

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