4 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…
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…