Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System
Hwiyeol Jo, Taiwoo Park, Hyunwoo Lee, Nayoung Choi, Changbong Kim, Ohjoon Kwon, Donghyeon Jeon, Eui-Hyeon Lee
Abstract
Although there has been a growing interest among industries in integrating generative LLMs into their services, limited experience and scarcity of resources act as a barrier in launching and servicing large-scale LLM-based services. In this paper, we share our experiences in developing and operating generative AI models within a national-scale search engine, with a specific focus on the sensitiveness of user queries. We propose a taxonomy for sensitive search queries, outline our approaches, and present a comprehensive analysis report on sensitive queries from actual users. We believe that our experiences in launching generative AI search systems can contribute to reducing the barrier in building generative LLM-based services.
BibTeX
@inproceedings{jo-etal-2025-taxonomy,
title = "Taxonomy and Analysis of Sensitive User Queries in Generative {AI} Search System",
author = "Jo, Hwiyeol and
Park, Taiwoo and
Lee, Hyunwoo and
Choi, Nayoung and
Kim, Changbong and
Kwon, Ohjoon and
Jeon, Donghyeon and
Lee, Eui-Hyeon and
Shin, Kyoungho and
Lim, Sun Suk and
Kim, Kyungmi and
Lee, Jihye and
Kim, Sun",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.195/",
pages = "3514--3529",
ISBN = "979-8-89176-195-7"
}