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20232026
most citedSafety Alignment of Large Language Models via Contrasting Safe and Harmful Distributions

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation

Yuying Li, Leqi Zheng, Yongzi Yu +6

On-Policy distillation (OPD) in large language models is shifting from full-trace KL supervision toward more selective training paradigms. Recent OPD methods increasingly focus on…

cs.LG2026

Sample Transform Cost-Based Training-Free Hallucination Detector for Large Language Models

Zeyang Ding, Xinglin Hu, Jicong Fan

Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are accurate, lightweight, and broadly applicable. Sin…

cs.CR2024

DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization

Pucheng Dang, Xing Hu, Dong Li +3

Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safet…

cs.CL2024★ 2 cited

Safety Alignment of Large Language Models via Contrasting Safe and Harmful Distributions

Xiaoyun Zhang, Zhengyue Zhao, Wenxuan Shi +3

With the widespread application of Large Language Models (LLMs), it has become a significant concern to ensure their safety and prevent harmful responses. While current safe-alignm…

cs.CV2023

Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?

Zhengyue Zhao, Jinhao Duan, Kaidi Xu +5

Stable Diffusion has established itself as a foundation model in generative AI artistic applications, receiving widespread research and application. Some recent fine-tuning methods…