RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization
This work addresses the need for more accurate and personalized AI-driven tourism services in Tibet, representing an incremental improvement by adapting existing RAG methods to a specific domain.
The study tackled the problem of hallucinations and lack of personalization in large language models for Tibetan tourism by applying retrieval-augmented generation (RAG) technology with a vectorized database, resulting in significant improvements in fluency, accuracy, and relevance of content generation.
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.