Research on Tibetan Tourism Viewpoints Information Generation System Based on LLM

被引：1

作者：

Qi, Jinhu ^{[1
]}

Yan, Shuai ^{[1
]}

Zhang, Wentao ^{[2
]}

Zhang, Yibo ^{[3
]}

Liu, Zirui ^{[2
]}

Wang, Ke ^{[1
]}

机构：

[1] Chengdu Jincheng Coll, Dept Artificial Intelligence, Chengdu, Peoples R China

[2] Chengdu Jincheng Coll, Dept Software Engn, Chengdu, Peoples R China

[3] Chengdu Jincheng Coll, Dept Comp Sci, Chengdu, Peoples R China

来源：

2024 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND WIRELESS OPTICAL COMMUNICATIONS, ICWOC | 2024年

关键词：

LLM; Tibet Tourism; Information Generation; Model Optimization Efficiency; Multi-Structure Generation Evaluation;

D O I：

10.1109/ICWOC62055.2024.10684948

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：35 / 41

页数：7

共 16 条

[1]

Bai YL, 2024, Arxiv, DOI arXiv:2403.18058

[2]

Chafetz H, 2024, arXiv, DOI [10.48550/arXiv.2405.04333, DOI 10.48550/ARXIV.2405.04333]

[3]

Chen W, 2024, Arxiv, DOI [arXiv:2404.01549, 10.48550/arXiv.2404.01549]

[4]

Du XR, 2024, Arxiv, DOI [arXiv:2404.04167, 10.48550/arXiv.2404.04167]

[5]

Hong J., 2024, arXiv, DOI [10.48550/arXiv.2403.0769, DOI 10.48550/ARXIV.2403.0769]

[6]

Hu EJ, 2021, Arxiv, DOI [arXiv:2106.09685, 10.48550/arXiv.2106.09685]

[7]

Lavie A., 2007, METEOR: An automatic metric for MT evaluation with improved correlation with human judgments, DOI [10.3115/1626355.1626389, DOI 10.3115/1626355.1626389]

[8]

Ouyang L, 2022, ADV NEUR IN

[9] BLEU: a method for automatic evaluation of machine translation [J].

Papineni, K ;

Roukos, S ;

Ward, T ;

Zhu, WJ .

40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, 2002, :311-318

[10]

Sano K., 2021, Value co-creation and open innovation in urban smart tourism ecosystem development: A lens from service dominant logic

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