ALOHA用分层检索让多语言校园导览更准更快
ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval
- 分层检索+多语言处理，精准定位校园信息
- 支持12000+人次使用，响应比商用聊天机器人快
- 适合高校智能导览、跨语言服务场景
大型语言模型（LLMs）虽能通过对话获取信息，但公开服务仍难以满足师生对校园特定信息的查询需求，主要因模型缺乏领域知识，且搜索引擎在多语言和时效性方面受限。为此，我们提出ALOHA，一种基于分层检索的多语言校园导览智能体，并集成外部API实现交互式服务。人工评估与案例研究显示，该系统在多语言场景下能生成准确、及时且友好的回答，性能优于商业聊天机器人和搜索引擎。系统已上线运行，服务超过12,000人次。
原文摘要 · Abstract (English)
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.
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