Papers by Jihye Lee
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. |
Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service (2023.findings-emnlp)
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| Challenge: | Language-Model-as-a-Services (LMaaSs) support a variety of user tasks through in-context learning from prompts. |
| Approach: | They propose a lightweight automatic prompt generation method that meta-trains a prompt generation model to enable robust learning from the contexts created by the generated prompts. |
| Outcome: | The proposed method improves performance on unseen tasks by 19.4% compared to the state-of-the-art prompt generation method. |
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines (2026.acl-industry)
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| Challenge: | Existing methods that optimize for relevance overlook document trustworthiness . Generative information retrieval (GenIR) is a promising paradigm for retrieval tasks . |
| Approach: | They propose an Authority-aware Generative Retriever (AuthGR) that incorporates authority into GenIR. |
| Outcome: | The proposed framework improves authority and accuracy in real-world user engagement and reliability. |
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. |