12 citations · 17 across the 5 of their papers we have counts for
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cs.CL2026
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
Han Liu, Zhi Xu, Xiaotong Zhang +5
Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that…
cs.CL2026★ 4 cited
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Han Liu, Haotian Gao, Xiaotong Zhang +5
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective t…
cs.CL2026
RUQuant: Towards Refining Uniform Quantization for Large Language Models
Han Liu, Haotian Gao, Changya Li +4
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-traini…