  Generative Vision-Language Models (VLMs) are prone to generate
plausible-sounding textual answers that, however, are not always grounded in
the input image. We investigate this phenomenon, usually referred to as
"hallucination" and show that it stems from an excessive reliance on the
language prior. In particular, we show that as more tokens are generated, the
reliance on the visual prompt decreases, and this behavior strongly correlates
with the emergence of hallucinations. To reduce hallucinations, we introduce
Multi-Modal Mutual-Information Decoding (M3ID), a new sampling method for
prompt amplification. M3ID amplifies the influence of the reference image over
the language prior, hence favoring the generation of tokens with higher mutual
information with the visual prompt. M3ID can be applied to any pre-trained
autoregressive VLM at inference time without necessitating further training and
with minimal computational overhead. If training is an option, we show that
M3ID can be paired with Direct Preference Optimization (DPO) to improve the
model's reliance on the prompt image without requiring any labels. Our
empirical findings show that our algorithms maintain the fluency and linguistic
capabilities of pre-trained VLMs while reducing hallucinations by mitigating
visually ungrounded answers. Specifically, for the LLaVA 13B model, M3ID and
M3ID+DPO reduce the percentage of hallucinated objects in captioning tasks by
25% and 28%, respectively, and improve the accuracy on VQA benchmarks such as
POPE by 21% and 24%.
