ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval

TL;DR

The paper introduces ALOHA, a multilingual university orientation agent enhanced with hierarchical retrieval and external API integration, demonstrating superior performance and deployment success for over 12,000 users.

Contribution

It presents a novel multilingual agent with hierarchical retrieval and API integration tailored for university information, addressing domain-specific and multilingual challenges.

Findings

Outperforms commercial chatbots and search engines in accuracy and timeliness.

Successfully deployed for over 12,000 users.

Provides correct, timely, and user-friendly responses in multiple languages.

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.