Large Vision Language Models (LVLMs) have shown remarkable capabilities in
multimodal tasks like visual question answering or image captioning. However,
inconsistencies between the visual information and the generated text, a
phenomenon referred to as hallucinations, remain an unsolved problem with
regard to the trustworthiness of LVLMs. To address this problem, recent works
proposed to incorporate computationally costly Large (Vision) Language Models
in order to detect hallucinations on a sentence- or subsentence-level. In this
work, we introduce MetaToken, a lightweight binary classifier to detect
hallucinations on the token-level at negligible cost. Based on a statistical
analysis, we reveal key factors of hallucinations in LVLMs which have been
overseen in previous works. MetaToken can be applied to any open-source LVLM
without any knowledge about ground truth data providing a reliable detection of
hallucinations. We evaluate our method on four state-of-the-art LVLMs
demonstrating the effectiveness of our approach.

通过引入 MetaToken，该研究提出了一种轻量级二元分类器来在低成本下检测大规模视觉语言模型中的幻觉，揭示了之前研究中忽视的 LVLMs 幻觉的关键因素，并在四个最先进的 LVLM 上进行了评估，证明了该方法的有效性。