1 citations · 1 across the 4 of their papers we have counts for
4 papers
Beyond Token Positions: Safety Alignment Across Denoising Steps in Diffusion Language Models
Guoli Wang, Haonan Shi, Tu Ouyang +1
Diffusion large language models (dLLMs) generate text through iterative denoising rather than left-to-right decoding. This generation paradigm introduces two axes that can influenc…
Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
Guoli Wang, Haonan Shi, Tu Ouyang +1
Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains on…
EASE: Practical and Efficient Safety Alignment for Small Language Models
Haonan Shi, Guoli Wang, Tu Ouyang +1
Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct…
VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation
Bo Pan, Yixiao Fu, Ke Wang +15
Data visualization generation using Large Language Models (LLMs) has shown promising results but often produces suboptimal visualizations that require human intervention for improv…