12 papers
Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference
Sunghyun Wee, Geunjae Choi, Hyeonjin Kim +2
Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-training quanti…
Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors
Junhoo Lee, Hyeonjin Kim, Sangbum Han +1
Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utility depends on generalizing b…
-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…
FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation
Hyeonjin Kim, Minseok Kim, Seunghyeon Jung +3
Traditional finance relies on experts to hand-craft factors through a principled process grounded in economic rationale. Recent LLM-based multi-agent systems have automated this pr…
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…
SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks
Seungwon Jeong, Jiwoo Jeong, Hyeonjin Kim +2
As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical. Optimization-based attacks like Greedy…
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…
Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning
Hyeonjin Kim, Hangyeol Jung, Heechan Yun +2
Unlearning specific concepts in text-to-image diffusion models has become increasingly important for preventing undesirable content generation. Among prior approaches, sparse autoe…
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…