@inproceedings{tao-etal-2025-aloha,
    title = "{ALOHA}: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval",
    author = "Tao, Mingxu  and
      Tang, Bowen  and
      Ma, Mingxuan  and
      Zhang, Yining  and
      Li, Hourun  and
      Wen, Feifan  and
      Hao, Ma  and
      Yang, Jia",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-demo.32/",
    doi = "10.18653/v1/2025.naacl-demo.32",
    pages = "383--392",
    ISBN = "979-8-89176-191-9",
    abstract = "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."
}