MetaToken：通过元分类检测图像描述中的幻觉
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification
AI总结：
提出轻量级分类器MetaToken，在令牌级别低成本检测LVLM图像描述中的幻觉，无需真实数据，并在四个先进模型上验证有效性。
摘要：
大型视觉语言模型（LVLMs）在多模态任务（如视觉问答或图像描述）中展现了卓越的能力。然而，视觉信息与生成文本之间的不一致性（即所谓的幻觉现象）仍然是关乎LVLMs可信度的一个未解问题。为解决此问题，近期工作提出引入计算成本高昂的大型（视觉）语言模型，以在句子或子句级别检测幻觉。在本工作中，我们引入了MetaToken，一种轻量级二元分类器，以极低的成本在令牌级别检测幻觉。基于统计分析，我们揭示了LVLMs中幻觉的关键因素。MetaToken可应用于任何开源LVLM，无需任何关于真实数据的信息，即可提供校准的幻觉检测。我们在四个最先进的LVLM上评估了我们的方法，证明了我们方法的有效性。
英文摘要：
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. MetaToken can be applied to any open-source LVLM without any knowledge about ground truth data providing a calibrated detection of hallucinations. We evaluate our method on four state-of-the-art LVLMs demonstrating the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
