Taxonomy and Analysis of Sensitive User Queries in Generative AI Search
Jo, Hwiyeol, Park, Taiwoo, Choi, Nayoung, Kim, Changbong, Kwon, Ohjoon, Jeon, Donghyeon, Lee, Hyunwoo, Lee, Eui-Hyeon, Shin, Kyoungho, Lim, Sun Suk, Kim, Kyungmi, Lee, Jihye, Kim, Sun
–arXiv.org Artificial Intelligence
Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational 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.
arXiv.org Artificial Intelligence
Apr-5-2024
- Country:
- Asia
- Japan (0.04)
- Middle East > Israel (0.04)
- South Korea (0.04)
- Europe > Croatia
- Dubrovnik-Neretva County > Dubrovnik (0.04)
- North America
- Dominican Republic (0.04)
- United States > Minnesota
- Hennepin County > Minneapolis (0.14)
- Asia
- Genre:
- Research Report (0.50)
- Industry:
- Health & Medicine (1.00)
- Information Technology > Security & Privacy (0.68)
- Law (0.95)
- Media > News (0.46)
- Technology: