hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

2026-08-27

AI Summary

为解决大学学术咨询中因学生个人情况不同导致的相同问题需不同答案的问题，提出proFILL方法，通过逐步获取必要用户信息来优化hoBIT聊天机器人的回复准确性。

Abstract

In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree program, causing profile-blind retrievers to surface plausible but inapplicable evidence. We present proFILL, a method for transforming hoBIT, our college's current rule-based advising chatbot, into a profile-aware retrieval-augmented generation (RAG) system. Rather than requiring a complete user profile upfront, proFILL progressively acquires only the profile attributes needed for each query, guided by both the query intent and the initially retrieved evidence, and uses them to condition retrieval over a profile-aware index. Extensive experiments and a human preference study show that proFILL outperforms diverse RAG baselines, is preferred by target users, and remains effective with open-weight models for cost-effective on-premise deployment.

Problem

• university academic advising
• profile-aware retrieval
• retrieval-augmented generation

Innovation

• profile-aware retrieval
• retrieval-augmented generation (RAG)
• progressive attribute acquisition