5 papers
Towards Provably Secure Generative AI: Reliable Consensus Sampling
Yu Cui, Hang Fu, Sicheng Pan +9
Existing research on generative AI security is primarily driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. This dynamic frequently g…
Can LLMs Threaten Human Survival? Benchmarking Potential Existential Threats from LLMs via Prefix Completion
Yu Cui, Yifei Liu, Hang Fu +4
Research on the safety evaluation of large language models (LLMs) has become extensive, driven by jailbreak studies that elicit unsafe responses. Such response involves information…
Free-MAD: Consensus-Free Multi-Agent Debate
Yu Cui, Hang Fu, Haibin Zhang +2
Multi-agent debate (MAD) is an emerging approach to improving the reasoning capabilities of large language models (LLMs). Existing MAD methods rely on multiple rounds of interactio…
SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents
Jiaye Lin, Yifu Guo, Yuzhen Han +11
Large Language Model (LLM)-based agents have recently shown impressive capabilities in complex reasoning and tool use via multi-step interactions with their environments. While the…
Ramp Up NTT in Record Time using GPU-Accelerated Algorithms and LLM-based Code Generation
Yu Cui, Hang Fu, Licheng Wang +1
Homomorphic encryption (HE) is a core building block in privacy-preserving machine learning (PPML), but HE is also widely known as its efficiency bottleneck. Therefore, many GPU-ac…