activity
20232026
most citedKorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge

2 citations · 2 across the 10 of their papers we have counts for

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Showing 2026Show all

9 papers · 1 filter

cs.LG2026

Guard Models Are Overconfident Where Base Models Are Uncertain

Jonghyun Hong, MinJae Jung, Minwoo Kim

Guard models are used as safety classifiers, with confidence scores driving downstream moderation decisions. We evaluate five guard models for prompt classification and find that a…

cs.AI2026

SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality

Hyunwoo Kim, Byoungchan Ko, Minseok Kang +5

Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providing a viable alternative to Tr…

cs.CV2026

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

Minseok Kang, Hyunwoo Kim, Chanyoung Kim +3

Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational a…

cs.CR2026

FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming

Chaeyun Kim, Daeyoung Park, Junghwan Kim +4

Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks. Financial LLMs face regulatory compliance violations, fraud facilitation, and syste…

cs.CL2026

Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis

Hyeji Choi, Yongtaek Lim, Minwoo Kim

Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target languages - an approach that co…

cs.AI2026

Reliable to Expressive: A Curriculum for Rubric-Following Safety Judges

Yongtaek Lim, Hyeji Choi, Minwoo Kim

Safety judges are increasingly deployed to evaluate model outputs against evolving criteria, yet recent meta-evaluation work shows they remain brittle under prompt and rubric varia…

cs.CL2026

Korean Culture into LLM Alignment: Toward Cultural Coherence

MinJae Jung, Minwoo Kim

Cultural-aspect work on large language models is dominated by a negative target: which outputs to suppress. We argue that a constructive counterpart is also needed, a working defin…

cs.CL2026

STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming

MinJae Jung, YongTaek Lim, Chaeyun Kim +3

While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. This paper introduces STAR-Team…

cs.CY2026

CAGE: A Framework for Culturally Adaptive Red-Teaming Benchmark Generation

Chaeyun Kim, YongTaek Lim, Kihyun Kim +2

Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in local culture and law, creating a c…