  Recent interest in Large Vision-Language Models (LVLMs) for practical
applications is moderated by the significant challenge of hallucination or the
inconsistency between the factual information and the generated text. In this
paper, we first perform an in-depth analysis of hallucinations and discover
several novel insights about how and when LVLMs hallucinate. From our analysis,
we show that: (1) The community's efforts have been primarily targeted towards
reducing hallucinations related to visual recognition (VR) prompts (e.g.,
prompts that only require describing the image), thereby ignoring
hallucinations for cognitive prompts (e.g., prompts that require additional
skills like reasoning on contents of the image). (2) LVLMs lack visual
perception, i.e., they can see but not necessarily understand or perceive the
input image. We analyze responses to cognitive prompts and show that LVLMs
hallucinate due to a perception gap: although LVLMs accurately recognize visual
elements in the input image and possess sufficient cognitive skills, they
struggle to respond accurately and hallucinate. To overcome this shortcoming,
we propose Visual Description Grounded Decoding (VDGD), a simple, robust, and
training-free method for alleviating hallucinations. Specifically, we first
describe the image and add it as a prefix to the instruction. Next, during
auto-regressive decoding, we sample from the plausible candidates according to
their KL-Divergence (KLD) to the description, where lower KLD is given higher
preference. Experimental results on several benchmarks and LVLMs show that VDGD
improves significantly over other baselines in reducing hallucinations. We also
propose VaLLu, a benchmark for the comprehensive evaluation of the cognitive
capabilities of LVLMs.
