2 citations · 2 across the 1 of their papers we have counts for
4 papers
Towards provable probabilistic safety for scalable embodied AI systems
Linxuan He, Lingxiang Fan, Qing-Shan Jia +13
Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety i…
Identifying and Understanding Cross-Class Features in Adversarial Training
Zeming Wei, Yiwen Guo, Yisen Wang
Adversarial training (AT) has been considered one of the most effective methods for making deep neural networks robust against adversarial attacks, while the training mechanisms an…
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
Taiye Chen, Zeming Wei, Ang Li +1
Large Language Models (LLMs) are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses.…
Advancing LLM Safe Alignment with Safety Representation Ranking
Tianqi Du, Zeming Wei, Quan Chen +2
The rapid advancement of large language models (LLMs) has demonstrated milestone success in a variety of tasks, yet their potential for generating harmful content has raised signif…