2 citations · 2 across the 10 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation
Jeongsoo Lee, Daeyong Kwon, Kyohoon Jin +3
Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations. A robust benchmark dataset must satisfy th…
KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge
Jiyoung Lee, Minwoo Kim, Seungho Kim +4
For Large Language Models (LLMs) to be effectively deployed in a specific country, they must possess an understanding of the nation's culture and basic knowledge. To this end, we i…
Analyzing Norm Violations in Live-Stream Chat
Jihyung Moon, Dong-Ho Lee, Hyundong Cho +6
Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and no…