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.
View on arXiv@article{tao2025_2505.08130,
title={ ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval },
author={ Mingxu Tao and Bowen Tang and Mingxuan Ma and Yining Zhang and Hourun Li and Feifan Wen and Hao Ma and Jia Yang },
journal={arXiv preprint arXiv:2505.08130},
year={ 2025 }
}