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    <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.</abstract>
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%0 Conference Proceedings
%T hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising
%A Yoonseo, Kim
%A Lee, Seongmin
%A Kim, Joongheon
%A Kang, SeongKu
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F yoonseo-etal-2026-hobit
%X 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.
%U https://aclanthology.org/2026.emnlp-demos.58/
%P 604-613

Markdown (Informal)

[hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising](https://aclanthology.org/2026.emnlp-demos.58/) (Yoonseo et al., EMNLP 2026)

• hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising (Yoonseo et al., EMNLP 2026)

ACL

• Kim Yoonseo, Seongmin Lee, Joongheon Kim, and SeongKu Kang. 2026. hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 604–613, Budapest, Hungary. Association for Computational Linguistics.