Kavli Affiliate: Ke Wang
| First 5 Authors: Jinhu Qi, Shuai Yan, Yibo Zhang, Wentao Zhang, Rong Jin
| Summary:
With the development of the modern social economy, tourism has become an
important way to meet people’s spiritual needs, bringing development
opportunities to the tourism industry. However, existing large language models
(LLMs) face challenges in personalized recommendation capabilities and the
generation of content that can sometimes produce hallucinations. This study
proposes an optimization scheme for Tibet tourism LLMs based on
retrieval-augmented generation (RAG) technology. By constructing a database of
tourist viewpoints and processing the data using vectorization techniques, we
have significantly improved retrieval accuracy. The application of RAG
technology effectively addresses the hallucination problem in content
generation. The optimized model shows significant improvements in fluency,
accuracy, and relevance of content generation. This research demonstrates the
potential of RAG technology in the standardization of cultural tourism
information and data analysis, providing theoretical and technical support for
the development of intelligent cultural tourism service systems.
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