2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2025
Safety-Aligned Weights Are Not Enough: Refusal-Teacher-Guided Finetuning Enhances Safety and Downstream Performance under Harmful Finetuning Attacks
Seokil Ham, Yubin Choi, Yujin Yang +3
While Finetuning-as-a-Service (FaaS) enables customization of Large Language Models (LLMs) using user data, this service is vulnerable to safety degradation when user data includes…
cs.CV2024★ 1 cited
RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
Sangmin Woo, Jaehyuk Jang, Donguk Kim +2
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produc…
cs.CV2024★ 2 cited
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Sangmin Woo, Donguk Kim, Jaehyuk Jang +2
Large Vision Language Models (LVLMs) demonstrate strong capabilities in visual understanding and description, yet often suffer from hallucinations, attributing incorrect or mislead…