  Large vision-language models (LVLMs) have recently dramatically pushed the
state of the art in image captioning and many image understanding tasks (e.g.,
visual question answering). LVLMs, however, often \textit{hallucinate} and
produce captions that mention concepts that cannot be found in the image. These
hallucinations erode the trustworthiness of LVLMs and are arguably among the
main obstacles to their ubiquitous adoption. Recent work suggests that addition
of grounding objectives -- those that explicitly align image regions or objects
to text spans -- reduces the amount of LVLM hallucination. Although intuitive,
this claim is not empirically justified as the reduction effects have been
established, we argue, with flawed evaluation protocols that (i) rely on data
(i.e., MSCOCO) that has been extensively used in LVLM training and (ii) measure
hallucination via question answering rather than open-ended caption generation.
In this work, in contrast, we offer the first systematic analysis of the effect
of fine-grained object grounding on LVLM hallucination under an evaluation
protocol that more realistically captures LVLM hallucination in open
generation. Our extensive experiments over three backbone LLMs reveal that
grounding objectives have little to no effect on object hallucination in open
caption generation.
