  In the realms of computer vision and natural language processing, Large
Vision-Language Models (LVLMs) have become indispensable tools, proficient in
generating textual descriptions based on visual inputs. Despite their
advancements, our investigation reveals a noteworthy bias in the generated
content, where the output is primarily influenced by the underlying Large
Language Models (LLMs) prior rather than the input image. Our empirical
experiments underscore the persistence of this bias, as LVLMs often provide
confident answers even in the absence of relevant images or given incongruent
visual input. To rectify these biases and redirect the model's focus toward
vision information, we introduce two simple, training-free strategies. Firstly,
for tasks such as classification or multi-choice question-answering (QA), we
propose a ``calibration'' step through affine transformation to adjust the
output distribution. This ``Post-Hoc debias'' approach ensures uniform scores
for each answer when the image is absent, serving as an effective
regularization technique to alleviate the influence of LLM priors. For more
intricate open-ended generation tasks, we extend this method to ``Debias
sampling'', drawing inspirations from contrastive decoding methods.
Furthermore, our investigation sheds light on the instability of LVLMs across
various decoding configurations. Through systematic exploration of different
settings, we significantly enhance performance, surpassing reported results and
raising concerns about the fairness of existing evaluations. Comprehensive
experiments substantiate the effectiveness of our proposed strategies in
mitigating biases. These strategies not only prove beneficial in minimizing
hallucinations but also contribute to the generation of more helpful and
precise illustrations.
