A Pilot Study on Doubt Robustness of LLMs in Clinical Prediction Explanation

A Pilot Study on Doubt Robustness of LLMs in Clinical Prediction ExplanationJuhwan Choi, Sangchul Hahn, Eunho Yang  We study large language models (LLMs) as clinical explanation generators and evaluate their robustness to user doubt in interactive settings. Using an in-hospital mortality p...

Status-Aware Self-Supervised Forecasting for Irregular Clinical Time Series

Status-Aware Self-Supervised Forecasting for Irregular Clinical Time SeriesKwanhyung Lee, Joohyung Lee, Jong-Heon Kim, Sangchul Hahn, Eunho Yang Electronic health record (EHR) time series are irregular event streams, and many clinical tasks require forecasting a patient’s future state from thei...

Structure-Aware Set Transformers: Temporal and Variable-type Attention Biases for Asynchronous Clinical Time Series

Structure-Aware Set Transformers: Temporal and Variable-type Attention Biases for Asynchronous Clinical Time SeriesJoohyung Lee, Kwanhyung Lee, Changhun Kim, Eunho Yang Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increas...

Deep learning-based real-time seizure detection and multi-seizure classification on pediatric EEG

Deep learning-based real-time seizure detection and multi-seizure classification on pediatric EEGHyewon Jeong, Kwanhyung Lee, Seyun Kim Hoon-Chul Kang, Donghwa Yang Background and objective: To develop a reliable and accurate seizure detection method using deep learning models capable of d...

SCAD: Super-Class-Aware Debiasing for Long-Tailed Semi-Supervised Learning

SCAD: Super-Class-Aware Debiasing for Long-Tailed Semi-Supervised LearningSunguk Jang*, Jinwoo Jeon*, Byung-Jun Lee (*: equal contribution)In long-tailed semi-supervised learning (LTSSL), pseudolabeling often creates a vicious cycle of bias amplification, a problem that recent state-of-the-art metho...

Soft Equivariance Regularization for Invariant Self-Supervised Learning

Soft Equivariance Regularization for Invariant Self-Supervised LearningJoohyung Lee, Changhun Kim, Hyunsu Kim, Kwanhyung Lee, Juho LeeA central principle in self-supervised learning (SSL) is to learn data representations that are invariant to semantic-preserving transformations eg, image representat...

Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring

Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series MonitoringChanghun Kim, Yechan Mun, Hyeongwon Jang, Eunseo Lee, Sangchul Hahn, Eunho YangExplaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where te...

Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models

Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language ModelsSangmin Song, Juhwan Choi, JungMin Yun, YoungBin KimLarge language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for t...

CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples

  Accepted at EMNLP 2025CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic TriplesKyohoon Jin, Juhwan Choi, Jungmin Yun, Junho Lee, Soojin Jang, Youngbin KimDeep learning models often learn and exploit spurious correlations in training data, using...

Medal Matters: Probing LLMs’ Failure Cases Through Olympic Rankings

 COLM 2025 ORLGen WorkshopMedal Matters: Probing LLMs’ Failure Cases Through Olympic RankingsJuhwan Choi, Seunguk Yu, JungMin Yun, YoungBin KimLarge language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures re...