9 citations · 16 across the 16 of their papers we have counts for
30 papers
Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer
Dongcheng Zhao, Sicheng Shen, Zhenyu Yang +6
Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary query and key representations map continuous similarities to sparse an…
Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron
Sicheng Shen, Mingyang Lv, Han Shen +7
The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved throu…
FireFly-P: FPGA-Accelerated Spiking Neural Network Plasticity for Robust Adaptive Control
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking Neural Networks (SNNs) offer a biologically plausible learning mechanism through synaptic plasticity, enabling unsupervised adaptation without the computational overhead of…
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike a…
Efficient LLM Safety Evaluation through Multi-Agent Debate
Dachuan Lin, Guobin Shen, Zihao Yang +3
Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether struct…
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong +3
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective tr…