7 papers
The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
Chen Chen, Xiang Gao, Xianshun Wang +6
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive layers to a server while keepi…
SoK: Security and Privacy of Foundation-Model-Powered Robots
Xueluan Gong, Chen Chen, Jinxin Liu +2
Foundation models are reshaping robotics by enabling robots to interpret open-ended instructions, reason over multimodal contexts, and operate in complex, open-world environments.…
Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems
Jiaxin Gao, Chen Chen, Yanwen Jia +3
Large Language Models (LLMs) are increasingly being used to autonomously evaluate the quality of content in communication systems, e.g., to assess responses in telecom customer sup…
Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations
Chen Chen, Yuchen Sun, Jiaxin Gao +4
Large language models (LLMs) are increasingly deployed in security-sensitive applications, yet remain vulnerable to backdoor attacks. However, existing backdoor defenses are diffic…
LLMs Cannot Reliably Judge (Yet?): A Comprehensive Assessment on the Robustness of LLM-as-a-Judge
Songze Li, Chuokun Xu, Jiaying Wang +6
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse tasks, driving the development and widespread adoption of LLM-as-a-Judge systems for automate…
ARMOR: Shielding Unlearnable Examples against Data Augmentation
Xueluan Gong, Yuji Wang, Yanjiao Chen +6
Private data, when published online, may be collected by unauthorized parties to train deep neural networks (DNNs). To protect privacy, defensive noises can be added to original sa…