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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08130 |
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| _version_ | 1866915286229188608 |
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| author | Tao, Mingxu Tang, Bowen Ma, Mingxuan Zhang, Yining Li, Hourun Wen, Feifan Ma, Hao Yang, Jia |
| author_facet | Tao, Mingxu Tang, Bowen Ma, Mingxuan Zhang, Yining Li, Hourun Wen, Feifan Ma, Hao Yang, Jia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval Tao, Mingxu Tang, Bowen Ma, Mingxuan Zhang, Yining Li, Hourun Wen, Feifan Ma, Hao Yang, Jia Computation and Language Artificial Intelligence 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. |
| title | ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.08130 |