更加关注图像：一种无需训练即可缓解大规模视觉语言模型幻觉的方法
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
AI总结：
本文针对LVLMs中因文本惯性导致的幻觉问题，提出一种无需训练的方法，通过放大图像token注意力权重并减去纯文本logits来增强视觉关注，实验证明可显著减少幻觉输出。
摘要：
现有的大规模视觉语言模型（LVLMs）主要将视觉编码器的图像特征与大型语言模型（LLMs）对齐，以利用其优越的文本生成能力。然而，视觉编码器与语言模型之间的规模差异可能导致LLMs在多模态理解中占据主导地位。这种LVLMs中的不平衡可能导致幻觉实例的出现。具体而言，LVLMs在有或没有视觉输入的情况下都可能生成一致的描述，这表明某些输出仅受上下文文本的影响。我们将这种现象称为“文本惯性”。为了解决这一问题，我们引入了一种无需训练的算法，以在图像理解与语言推理之间找到平衡点。具体来说，我们自适应地调整并放大分配给图像token的注意力权重，从而赋予视觉元素更大的显著性。同时，我们从纯文本输入的logits中减去多模态输入的logits，这有助于LVLMs不偏向LLMs。通过增强图像token并减少LLM的顽固输出，我们可以让LVLM更加关注图像，从而缓解文本惯性并减少LVLMs中的幻觉。我们的大量实验表明，该方法在不同指标上显著降低了各种LVLMs中幻觉输出的频率。项目页面可在 https://lalbj.github.io/projects/PAI/ 获取。
英文摘要：
Existing Large Vision-Language Models (LVLMs) primarily align image features of vision encoder with Large Language Models (LLMs) to leverage their superior text generation capabilities. However, the scale disparity between vision encoder and language model may led to LLMs assuming a predominant role in multi-modal comprehension. This imbalance in LVLMs may result in the instances of hallucinatory. Concretely, LVLMs may generate consistent descriptions with or without visual input, indicating that certain outputs are influenced solely by context text. We refer to this phenomenon as "text inertia." To counteract this issue, we introduce a training-free algorithm to find an equilibrium point between image comprehension and language inference. Specifically, we adaptively involve adjusting and amplifying the attention weights assigned to image tokens, thereby granting greater prominence to visual elements. Meanwhile, we subtract the logits of multi-modal inputs from ones of pure text input, which can help LVLMs be not biased towards LLMs. By enhancing images tokens and reducing the stubborn output of LLM, we can let LVLM pay more attention to images, towards alleviating text inertia and reducing the hallucination in LVLMs. Our extensive experiments shows that this method substantially reduces the frequency of hallucinatory outputs in various LVLMs in terms of different metrics. Project page is available at https://lalbj.github.io/projects/PAI/.
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。
