Kavli Affiliate: Ke Wang
| First 5 Authors: Jinhu Qi, Shuai Yan, Wentao Zhang, Yibo Zhang, Zirui Liu
| Summary:
Tibet, ensconced within China’s territorial expanse, is distinguished by its
labyrinthine and heterogeneous topography, a testament to its profound
historical heritage, and the cradle of a unique religious ethos. The very
essence of these attributes, however, has impeded the advancement of Tibet’s
tourism service infrastructure, rendering existing smart tourism services
inadequate for the region’s visitors. This study delves into the ramifications
of informational disparities at tourist sites on Tibetan tourism and addresses
the challenge of establishing the Large Language Model (LLM) evaluation
criteria. It introduces an innovative approach, the DualGen Bridge AI system,
employing supervised fine-tuning techniques to bolster model functionality and
enhance optimization processes. Furthermore, it pioneers a multi-structured
generative results assessment framework. Empirical validation confirms the
efficacy of this framework. The study also explores the application of the
supervised fine-tuning method within the proprietary DualGen Bridge AI, aimed
at refining the generation of tourist site information. The study’s findings
offer valuable insights for optimizing system performance and provide support
and inspiration for the application of LLM technology in Tibet’s tourism
services and beyond, potentially revolutionizing the smart tourism industry
with advanced, tailored information generation capabilities.
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