ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval
This addresses the need for multilingual and timely campus information retrieval for university communities, but it is incremental as it builds on existing LLM and retrieval methods.
The paper tackled the problem of inadequate campus-specific information retrieval for faculty and students by introducing ALOHA, a multilingual agent with hierarchical retrieval for university orientation, which was deployed and served over 12,000 people, showing strong capabilities in providing correct, timely, and user-friendly responses in multiple languages.
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.