A new method, FADE, has been proposed to mitigate hallucinations in Large Vision-Language Models (LVLMs) by reducing language-prior dominance. This is achieved through a training-free approach that attenuates the outputs of feed-forward networks (FFNs), which are found to be the source of language priors. Evaluations show that FADE effectively reduces hallucinations while preserving inference efficiency.