Cheng Deng

Large language models (LLMs) store extensive factual knowledge acquired during pretraining, yet this knowledge is inherently static and may become inaccurate or outdated, leading to knowledge hallucinations. Knowledge editing offers an efficient alternative to full retraining by enabling targeted...

High-resolution visual tokens impose substantial computational burdens owing to extreme redundancy in Large Visual Language Models (LVLMs). Existing visual token pruning methods typically leverage simple metrics derived from human experience, such as attention or similarity, to rank and select to...

LVLMs have achieved strong multimodal reasoning capabilities but remain prone to hallucinations, producing outputs inconsistent with visual inputs or user instructions. Existing training-free methods, including contrastive decoding and auxiliary expert models, which incur several times more compu...

As Large Language Models (LLMs) become increasingly prevalent, their security vulnerabilities have already drawn attention.Machine unlearning is introduced to seek to mitigate these risks by removing the influence of undesirable data. However, existing methods not only rely on the retained datase...