@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/",
doi = "10.18653/v1/2025.findings-naacl.195",
pages = "3514--3529",
ISBN = "979-8-89176-195-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System
%A Jo, Hwiyeol
%A Park, Taiwoo
%A Lee, Hyunwoo
%A Choi, Nayoung
%A Kim, Changbong
%A Kwon, Ohjoon
%A Jeon, Donghyeon
%A Lee, Eui-Hyeon
%A Shin, Kyoungho
%A Lim, Sun Suk
%A Kim, Kyungmi
%A Lee, Jihye
%A Kim, Sun
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F jo-etal-2025-taxonomy
%X 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.
%R 10.18653/v1/2025.findings-naacl.195
%U https://aclanthology.org/2025.findings-naacl.195/
%U https://doi.org/10.18653/v1/2025.findings-naacl.195
%P 3514-3529
Markdown (Informal)
[Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System](https://aclanthology.org/2025.findings-naacl.195/) (Jo et al., Findings 2025)
ACL
- Hwiyeol Jo, Taiwoo Park, Hyunwoo Lee, Nayoung Choi, Changbong Kim, Ohjoon Kwon, Donghyeon Jeon, Eui-Hyeon Lee, Kyoungho Shin, Sun Suk Lim, Kyungmi Kim, Jihye Lee, and Sun Kim. 2025. Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 3514–3529, Albuquerque, New Mexico. Association for Computational Linguistics.