Papers by Lim Sun Suk
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System (2025.findings-naacl)
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Hwiyeol Jo, Taiwoo Park, Hyunwoo Lee, Nayoung Choi, Changbong Kim, Ohjoon Kwon, Donghyeon Jeon, Eui Hyeon Lee, Kyoungho Shin, Lim Sun Suk, Kyungmi Kim, Lee Jihye, Sun Kim
| Challenge: | generative LLMs have been used by industries for various purposes, but limited resources and limited experience hinder their deployment and maintenance. |
| Approach: | They propose a taxonomy for sensitive search queries and outline approaches to generating generative LLMs. |
| Outcome: | The proposed model can be used to analyze sensitive queries from real users. |
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines (2025.acl-industry)
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| Challenge: | Large language models (LLMs) have been widely used for relevance assessment in information retrieval, but maintaining and updating such models is resource-intensive, limiting their feasibility in dynamic and multilingual search environments. |
| Approach: | They propose to combine a generative SLM with an embedding-based SLM to achieve higher relevance judgment accuracy while reducing computational costs. |
| Outcome: | The proposed approach outperforms state-of-the-art LLMs in relevance assessment tasks while reducing computational costs. |