4 papers
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
Kai Tang, Jinhao You, Yichen Guo +8
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated content is inconsistent with the input image…
Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
Hanze Li, Jinhao You, Yichen Guo +3
Large Language Models (LLMs) have achieved strong performance across diverse natural language tasks, yet their outputs often suffer from hallucinations -- content that is misaligne…
Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals
Hanze Li, Yaosong Du, Zhibo Yao +3
Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly advanced network architectures. Howeve…
STAR: Stage-Wise Attention-Guided Token Reduction for Efficient Large Vision-Language Models Inference
Yichen Guo, Hanze Li, Zonghao Zhang +3
Although large vision-language models (LVLMs) leverage rich visual token representations to achieve strong performance on multimodal tasks, these tokens also introduce significant…