RAG优化的西藏旅游大型语言模型：提升准确性与个性化
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization
AI总结：
针对LLM在西藏旅游场景中的个性化不足与生成幻觉问题，本文构建游客观点向量库并引入RAG，提升了生成内容的准确性、流畅性与相关性。
摘要：
随着现代社会经济的发展，旅游已成为满足人们精神需求的重要方式，并为旅游业带来发展机遇。然而，现有大型语言模型（LLM）在个性化推荐能力方面面临挑战，且其生成内容有时会产生幻觉。本研究提出一种基于检索增强生成（RAG）技术的西藏旅游LLM优化方案。通过构建游客观点数据库并利用向量化技术处理数据，显著提高了检索准确性。RAG技术的应用有效解决了内容生成中的幻觉问题。经优化的模型在内容生成的流畅性、准确性和相关性方面均表现出显著提升。本研究展示了RAG技术在文化旅游信息标准化和数据分析中的潜力，为智能文化旅游服务系统的发展提供了理论和技术支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
