1 citations · 1 across the 5 of their papers we have counts for
6 papers
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir +8
Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from binary verifiers or reward…
Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models
Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh +6
This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based appr…
MLLM-as-a-Judge for Image Safety without Human Labeling
Zhenting Wang, Shuming Hu, Shiyu Zhao +12
Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generati…
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation
Jianfa Chen, Emily Shen, Trupti Bavalatti +10
Robust content moderation classifiers are essential for the safety of Generative AI systems. In this task, differences between safe and unsafe inputs are often extremely subtle, ma…
Imagine yourself: Tuning-Free Personalized Image Generation
Zecheng He, Bo Sun, Felix Juefei-Xu +14
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…
T-HITL Effectively Addresses Problematic Associations in Image Generation and Maintains Overall Visual Quality
Susan Epstein, Li Chen, Alessandro Vecchiato +1
Generative AI image models may inadvertently generate problematic representations of people. Past research has noted that millions of users engage daily across the world with these…