  Despite achieving rapid developments and with widespread applications, Large
Vision-Language Models (LVLMs) confront a serious challenge of being prone to
generating hallucinations. An over-reliance on linguistic priors has been
identified as a key factor leading to these hallucinations. In this paper, we
propose to alleviate this problem by introducing a novel image-biased decoding
(IBD) technique. Our method derives the next-token probability distribution by
contrasting predictions from a conventional LVLM with those of an image-biased
LVLM, thereby amplifying the correct information highly correlated with image
content while mitigating the hallucinatory errors caused by excessive
dependence on text. We further conduct a comprehensive statistical analysis to
validate the reliability of our method, and design an adaptive adjustment
strategy to achieve robust and flexible handling under varying conditions.
Experimental results across multiple evaluation metrics verify that our method,
despite not requiring additional training data and only with a minimal increase
in model parameters, can significantly reduce hallucinations in LVLMs and
enhance the truthfulness of the generated response.
