1 citations · 2 across the 21 of their papers we have counts for
22 papers
ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation
Changhun Kim, Sunguk Jang, Jeongjun Lee +5
Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynam…
Not Safe for All: Auditing the Dialect Penalty in Text-to-Image Safety Pipelines
Minkyu Kim, Juhwan Choi, YoungBin Kim
Text-to-image (T2I) safety guardrails fail to generalize equitably to non-standard dialects. Evaluating 23,080 paired prompts across five English dialects, we formalize this failur…
Learning to Select Source-Traceable Evidence for Language-Model Prediction from Irregular Clinical Time Series
Kwanhyung Lee, Juhwan Choi, Jongheon Kim +5
Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns support each prediction as readabl…
FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation
Jinhee Jang, Juhwan Choi, Dongjin Lee +2
Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models exhibit systematic gender b…
SUMMPILOT: Bridging Efficiency and Customization for Interactive Summarization System
JungMin Yun, Juhwan Choi, Kyohoon Jin +3
This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requi…
EMR-AGENT: Automating Cohort and Feature Extraction from EMR Databases
Kwanhyung Lee, Sungsoo Hong, Joonhyung Park +4
Machine learning models for clinical prediction rely on structured data extracted from Electronic Medical Records (EMRs), yet this process remains dominated by hardcoded, database-…
CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples
Kyohoon Jin, Juhwan Choi, Jungmin Yun +3
Deep learning models often learn and exploit spurious correlations in training data, using these non-target features to inform their predictions. Such reliance leads to performance…
Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models
Seunguk Yu, Juhwan Choi, Youngbin Kim
Despite the recent strides in large language models, studies have underscored the existence of social biases within these systems. In this paper, we delve into the validation and c…
Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models
Sangmin Song, Juhwan Choi, JungMin Yun +1
Large language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. However, conventiona…
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models
Kyeonghyun Kim, Jinhee Jang, Juhwan Choi +3
Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable…
See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias
JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +2
Vision-language (VL) models have demonstrated strong performance across various tasks. However, these models often rely on a specific modality for predictions, leading to "dominant…
Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings
Juhwan Choi, Seunguk Yu, JungMin Yun +1
Large language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures remain poorly understood. This study exa…
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks
Juhwan Choi, Junehyoung Kwon, JungMin Yun +2
Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research…
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation
Juhwan Choi, Yeonghwa Kim, Seunguk Yu +2
Although pre-trained language models have exhibited great flexibility and versatility with prompt-based few-shot learning, they suffer from the extensive parameter size and limited…
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Juhwan Choi, Jungmin Yun, Kyohoon Jin +1
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included…
Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion
Juhwan Choi, YoungBin Kim
In the field of text data augmentation, rule-based methods are widely adopted for real-world applications owing to their cost-efficiency. However, conventional rule-based approache…
Colorful Cutout: Enhancing Image Data Augmentation with Curriculum Learning
Juhwan Choi, YoungBin Kim
Data augmentation is one of the regularization strategies for the training of deep learning models, which enhances generalizability and prevents overfitting, leading to performance…
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Kyohoon Jin, Junho Lee, Juhwan Choi +2
Efforts to leverage deep learning models in low-resource regimes have led to numerous augmentation studies. However, the direct application of methods such as mixup and cutout to t…
Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
Seunguk Yu, Juhwan Choi, Youngbin Kim
Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filte…
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Juhwan Choi, Kyohoon Jin, Junho Lee +2
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity. However, this method can potentially damage the original meaning of the text, ultimately hurti…
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
Juhwan Choi, Kyohoon Jin, Junho Lee +2
Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because…
GPTs Are Multilingual Annotators for Sequence Generation Tasks
Juhwan Choi, Eunju Lee, Kyohoon Jin +1
Data annotation is an essential step for constructing new datasets. However, the conventional approach of data annotation through crowdsourcing is both time-consuming and expensive…