基于检索增强生成的大型语言模型户外步道推荐聊天机器人的经验
Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
AI总结：
本文介绍了基于LLM与RAG技术开发的户外步道推荐聊天机器人Judy，通过美国康涅狄格州步道案例研究，验证了其在提供准确、高效且可用的个性化步道推荐方面的有效性。
摘要：
户外休闲活动（如徒步和骑行）的日益普及，增加了对能够提供户外步道信息丰富且个性化建议的对话式AI系统的需求。在以下方面面临挑战：（1）如何通过对话式AI提供准确的户外步道信息；（2）如何实现可用且高效的推荐服务。为解决上述问题，本文讨论了在开发Judy（一个基于大型语言模型（LLM）与检索增强生成（RAG）的户外步道推荐聊天机器人）过程中获得的初步和实用经验。为了获得具体的系统洞察，我们以美国康涅狄格州（CT）的户外步道进行了案例研究。我们开展了基于网络的数据收集、户外步道数据管理，以及基于RAG推荐的LLM模型性能研究。实验结果证明了Judy在基于LLM与RAG推荐户外步道方面的准确性、有效性和可用性。
英文摘要：
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challenges arise in response to (1) how to provide accurate outdoor trail information via conversational AI; and (2) how to enable usable and efficient recommendation services. To address above, this paper discusses the preliminary and practical lessons learned from developing Judy, an outdoor trail recommendation chatbot based on the large language model (LLM) with retrieval augmented generation (RAG). To gain concrete system insights, we have performed case studies with the outdoor trails in Connecticut (CT), US. We have conducted web-based data collection, outdoor trail data management, and LLM model performance studies on the RAG-based recommendation. Our experimental results have demonstrated the accuracy, effectiveness, and usability of Judy in recommending outdoor trails based on the LLM with RAG.
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。
