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Ning Zhang

ACL 2026×2ICLR 2026安全多模态对齐与偏好推理强化学习与后训练
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0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #3/4ACL 2026Poster· Natural Language Processing

Protecting Language Models Against Unauthorized Distillation through Trace Rewriting

Xinhang Ma · William Yeoh · Ning Zhang · Yevgeniy Vorobeychik

摘要

Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models.However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models.We investigate...

推理安全强化学习与后训练OpenReview 项目页
作者位次 #5/5ACL 2026Poster· Natural Language Processing

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring

Peichun Hua · Hao Li · Shanghao Shi · Zhiyuan Yu · Ning Zhang

摘要

Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and efficient for practical deployment. Many current strategies fall short, either targeting specific attack patterns, which...

安全可解释性多模态OpenReview 项目页

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