2 citations · 4 across the 13 of their papers we have counts for
5 papers · 1 filter
Auto-Prompt Generation is Not Robust: Prompt Optimization Driven by Pseudo Gradient
Zeru Shi, Zhenting Wang, Yongye Su +5
While automatic prompt generation methods have recently received significant attention, their robustness remains poorly understood. In this paper, we introduce PertBench, a compreh…
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Cheng Han, James Chenhao Liang +6
Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic i…
Disentangling Memory and Reasoning Ability in Large Language Models
Mingyu Jin, Weidi Luo, Sitao Cheng +5
Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM in…
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations
Huaizhi Ge, Yiming Li, Qifan Wang +2
Large Language Models (LLMs) are known to be vulnerable to backdoor attacks, where triggers embedded in poisoned samples can maliciously alter LLMs' behaviors. In this paper, we mo…
TrustAgent: Towards Safe and Trustworthy LLM-based Agents
Wenyue Hua, Xianjun Yang, Mingyu Jin +4
The rise of LLM-based agents shows great potential to revolutionize task planning, capturing significant attention. Given that these agents will be integrated into high-stake domai…