Papers by Joeun Kim
QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to grounding large language models rely on static weights and a static retrieval component. |
| Approach: | They propose a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |
| Outcome: | The proposed framework adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |
Subject-level Inference for Realistic Text Anonymization Evaluation (2026.acl-long)
Copied to clipboard
Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh, Chaean Kang, Joeun Kang, Xiaonan Wang, Hyunjung Park, Young Cheol Jung, Hansaem Kim
| Challenge: | Existing text anonymization evaluations assume only a single data subject, ignoring multi-subject scenarios. |
| Approach: | They propose a benchmark that shifts the unit of evaluation from text spans to individuals . they show that subject-level inference protection drops as low as 33% when masked . |
| Outcome: | The proposed benchmark reduces the amount of protection available when PII spans are masked. |