1 citations · 1 across the 7 of their papers we have counts for
8 papers
CPR for LLMs: Critical-Point Routing against Catastrophic Forgetting in Domain Adaptation
Kwangmin Ki, Yunhun Nam, Jongheon Jeong +1
Supervised fine-tuning (SFT) is the de facto standard for adapting large language models (LLMs) to target domains, but it often degrades the model's general capabilities, a phenome…
Vision-driven Preference Synthesis for Mitigating Hallucinations in VLMs
Yunhun Nam, Jongheon Jeong
Vision-Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that is not grounded in the image…
Persona-Pruner: Sculpting Lightweight Models for Role-Playing
Jinsu Kim, Jihoon Tack, Noah Lee +1
Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user perso…
Learning from the Undesirable: Robust Adaptation of Language Models without Forgetting
Yunhun Nam, Jaehyung Kim, Jongheon Jeong
Language models (LMs) are often adapted through supervised fine-tuning (SFT) to specialize their capabilities for downstream tasks. However, in typical scenarios where the fine-tun…
BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editing
Jinsu Kim, Yunhun Nam, Minseon Kim +2
Recent advances in text-to-image models have increased the exposure of powerful image editing techniques as a tool, raising concerns about their potential for malicious use. An eme…
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations
Hyunjong Kim, Sangyeop Kim, Jongheon Jeong +2
Recent advances in large language models and vision-language models have led to growing interest in explainable evaluation metrics for image captioning. However, these metrics gene…
Enhancing Variational Autoencoders with Smooth Robust Latent Encoding
Hyomin Lee, Minseon Kim, Sangwon Jang +2
Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain larg…
Margin-aware Preference Optimization for Aligning Diffusion Models without Reference
Jiwoo Hong, Sayak Paul, Noah Lee +3
Modern preference alignment methods, such as DPO, rely on divergence regularization to a reference model for training stability-but this creates a fundamental problem we call "refe…