7 papers
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
Minchan Kwon, Sunghyun Baek, Minseo Kim +3
Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in…
MDS-DETR: DETR with Masked Duplicate Suppressor
Chanho Lee, Seunghee Koh, Yunho Jeon +1
The DEtection TRansformer (DETR) is a powerful end-to-end object detector, yet its one-to-one matching strategy suffers from slow convergence and low recall. A common approach to a…
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens
Seunghee Koh, Sunghyun Baek, Youngdong Kim +1
Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied uniformly without considering tok…
ConceptPrism: Concept Disentanglement in Personalized Diffusion Models via Residual Token Optimization
Minseo Kim, Minchan Kwon, Dongyeun Lee +2
Personalized text-to-image (T2I) generation has emerged as a key application for creating user-specific concepts from a few reference images. The core challenge is concept disentan…
Comparison Reveals Commonality: Customized Image Generation through Contrastive Inversion
Minseo Kim, Minchan Kwon, Dongyeun Lee +2
The recent demand for customized image generation raises a need for techniques that effectively extract the common concept from small sets of images. Existing methods typically rel…
SFLD: Reducing the content bias for AI-generated Image Detection
Seoyeon Gye, Junwon Ko, Hyounguk Shon +2
Identifying AI-generated content is critical for the safe and ethical use of generative AI. Recent research has focused on developing detectors that generalize to unknown generator…