3 citations · 3 across the 13 of their papers we have counts for
13 papers
-Bench: A Multilingual Machine Unlearning Benchmark
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +3
Undesired information such as harmful content and private data propagates through Multilingual Large Language Models (LLMs) via direct training and indirect cross-linguistic spread…
Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models
Hyunho Lee, Kyomin Hwang, Hyeonjin Kim +3
With an increasing number of Object Detection (OD) models being deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the or…
Team MKC at CLPsych 2026: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
Kyomin Hwang, Hyeonjin Kim, Hyunho Lee +1
Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health. Given the gr…
ReSpinQuant: Efficient Layer-Wise LLM Quantization via Subspace Residual Rotation Approximation
Suyoung Kim, Sunghyun Wee, Hyeonjin Kim +3
Rotation-based Post-Training Quantization (PTQ) has emerged as a promising solution for mitigating activation outliers in the quantization of Large Language Models (LLMs). Global r…
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1
While LLMs are increasingly used in commercial services, they pose privacy risks such as leakage of sensitive personally identifiable information (PII). For LLMs trained on multili…
Retrieval-Augmented Generation Based Nurse Observation Extraction
Kyomin Hwang, Nojun Kwak
Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into th…
Safety-Preserving PTQ via Contrastive Alignment Loss
Sunghyun Wee, Suyoung Kim, Hyeonjin Kim +2
Post-Training Quantization (PTQ) has become the de-facto standard for efficient LLM deployment, yet its optimization objective remains fundamentally incomplete. Standard PTQ method…
Uncovering the Potential Risks in Unlearning: Danger of English-only Unlearning in Multilingual LLMs
Kyomin Hwang, Hyeonjin Kim, Seungyeon Kim +2
There have been a couple of studies showing that attempting to erase multilingual knowledge using only English data is insufficient for multilingual LLMs. However, their analyses r…
Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization
Dongkwan Lee, Kyomin Hwang, Nojun Kwak
We address the problem of semi-supervised domain generalization (SSDG), where the distributions of train and test data differ, and only a small amount of labeled data along with a…
Do not think about pink elephant!
Kyomin Hwang, Suyoung Kim, JunHoo Lee +1
Large Models (LMs) have heightened expectations for the potential of general AI as they are akin to human intelligence. This paper shows that recent large models such as Stable Dif…
Mitigating the Bias in the Model for Continual Test-Time Adaptation
Inseop Chung, Kyomin Hwang, Jayeon Yoo +1
Continual Test-Time Adaptation (CTA) is a challenging task that aims to adapt a source pre-trained model to continually changing target domains. In the CTA setting, a model does no…
Deep Support Vectors
Junhoo Lee, Hyunho Lee, Kyomin Hwang +1
Deep learning has achieved tremendous success. However, unlike SVMs, which provide direct decision criteria and can be trained with a small dataset, it still has significant weakne…
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…