ALOHA：利用分层检索赋能面向大学迎新的多语言智能体
ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval
AI总结：
本文提出ALOHA多语言智能体，通过分层检索和外部API解决校园信息缺乏领域知识、多语言及时检索不足的问题，已服务超12,000人。
摘要：
大型语言模型（LLM）的兴起彻底改变了信息检索，使用户能够在对话中通过复杂指令获得所需答案。然而，现有公开服务在满足教职工和学生检索校园特定信息需求方面仍然不足。这主要是由于LLM缺乏领域特定知识，以及搜索引擎在支持多语言和时效性场景方面存在局限。为应对这些挑战，我们提出了ALOHA，这是一个由分层检索增强、用于大学迎新的多语言智能体。我们还将外部API集成到前端界面中，以提供交互式服务。人工评估和案例研究表明，所提出的系统能够以多种语言对查询生成正确、及时且用户友好的回复，表现优于商业聊天机器人和搜索引擎。该系统已完成部署，并已为超过12,000人提供服务。
英文摘要：
The rise of Large Language Models~(LLMs) revolutionizes information retrieval, allowing users to obtain required answers through complex instructions within conversations. However, publicly available services remain inadequate in addressing the needs of faculty and students to search campus-specific information. It is primarily due to the LLM's lack of domain-specific knowledge and the limitation of search engines in supporting multilingual and timely scenarios. To tackle these challenges, we introduce ALOHA, a multilingual agent enhanced by hierarchical retrieval for university orientation. We also integrate external APIs into the front-end interface to provide interactive service. The human evaluation and case study show our proposed system has strong capabilities to yield correct, timely, and user-friendly responses to the queries in multiple languages, surpassing commercial chatbots and search engines. The system has been deployed and has provided service for more than 12,000 people.
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。
