Papers by Wooyoung Kim
XDAC: XAI-Driven Detection and Attribution of LLM-Generated News Comments in Korean (2025.acl-long)
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| Challenge: | Large language models generate human-like text, raising concerns about their misuse in creating deceptive content. |
| Approach: | They propose a framework for detecting LLM-generated comments in Korean news and introduce a XDAC framework that leverages explainable AI to uncover distinguishing linguistic patterns at token and character levels. |
| Outcome: | The proposed framework outperforms existing methods and achieves 98.5% F1 score in detection and 84.3% F1 in attribution. |
TELLME: Test-Enhanced Learning for Language Model Enrichment (2026.findings-eacl)
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Minjun Kim, Inho Won, HyeonSeok Lim, MinKyu Kim, Junghun Yuk, Wooyoung Go, Jongyoul Park, Jungyeul Park, KyungTae Lim
| Challenge: | Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models, but has faced challenges such as acquiring large-scale domain-specific datasets and high computational costs. |
| Approach: | They propose a method that integrates the Test-Enhanced Learning principle with CPT to promote efficient domain-specific knowledge acquisition and long-term memory retention. |
| Outcome: | The proposed method outperforms existing methods by 23.6% in the financial domain and achieves 9.8% improvement in long-term memory retention. |
Korean Disaster Safety Information Sign Language Translation Benchmark Dataset (2024.lrec-main)
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Wooyoung Kim, TaeYong Kim, Byeongjin Kim, Myeong Jin MJ Lee, Gitaek Lee, Kirok Kim, Jisoo Cha, Wooju Kim
| Challenge: | Sign language is a crucial means of communication for deaf communities. |
| Approach: | They propose to refine Korean sign language translation datasets and release them . they show baseline performance varies depending on tokenization method applied to gloss sequences . |
| Outcome: | The proposed dataset outperforms baseline and spoken language tokenization methods. |