  Vision-language models (VLMs) are impactful in part because they can be
applied to a variety of visual understanding tasks in a zero-shot fashion,
without any fine-tuning. We study $\textit{generative VLMs}$ that are trained
for next-word generation given an image. We explore their zero-shot performance
on the illustrative task of image-text retrieval across 8 popular
vision-language benchmarks. Our first observation is that they can be
repurposed for discriminative tasks (such as image-text retrieval) by simply
computing the match score of generating a particular text string given an
image. We call this probabilistic score the $\textit{Visual Generative
Pre-Training Score}$ (VisualGPTScore). While the VisualGPTScore produces
near-perfect accuracy on some retrieval benchmarks, it yields poor accuracy on
others. We analyze this behavior through a probabilistic lens, pointing out
that some benchmarks inadvertently capture unnatural language distributions by
creating adversarial but unlikely text captions. In fact, we demonstrate that
even a "blind" language model that ignores any image evidence can sometimes
outperform all prior art, reminiscent of similar challenges faced by the
visual-question answering (VQA) community many years ago. We derive a
probabilistic post-processing scheme that controls for the amount of linguistic
bias in generative VLMs at test time without having to retrain or fine-tune the
model. We show that the VisualGPTScore, when appropriately debiased, is a
strong zero-shot baseline for vision-language understanding, oftentimes
producing state-of-the-art accuracy.
