深入研究用于缓解大视觉语言模型幻觉的视觉对比解码
Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models
AI总结：
该研究针对大视觉语言模型的幻觉问题，深入探索多种视觉对比解码方法，分析不同对比样本的效果差异，提出一种简单有效的对比样本融合方法，经多基准实验验证可实用地缓解幻觉。
摘要：
尽管大视觉语言模型（LVLMs）在生成与输入视觉内容相关的合理回复方面已展现出令人瞩目的能力，但它们仍存在幻觉问题，即生成的文本无法准确反映视觉内容。为解决这一问题，近期的方法采用对比解码，通过对比原始样本与视觉失真样本的输出分布来校准模型的回复，以无需训练的方式在缓解幻觉方面取得了可观的效果。然而，视觉输入中信息变化的潜力尚未得到充分探索，因此深入研究视觉对比解码的行为具有重要意义。在本文中，我们首先探索了多种通过改变视觉内容实现对比解码的方法，包括图像下采样和图像编辑。图像下采样会减少详细的文本信息，而编辑则会在图像中产生新内容，为视觉对比样本提供了新的维度。为进一步研究使用不同对比样本带来的益处，我们分析了概率层面的指标，包括熵和分布距离。有趣的是，这些样本在缓解幻觉方面的效果在不同LVLMs和基准之间存在很大差异。基于我们的分析，我们提出了一种简单而有效的对比样本融合方法，为在各种场景下应用对比解码提供了实用的解决方案。我们在不同基准上开展了大量实验，以验证所提出的融合方法的有效性。
英文摘要：
While large vision-language models (LVLMs) have shown impressive capabilities in generating plausible responses correlated with input visual contents, they still suffer from hallucinations, where the generated text inaccurately reflects visual contents. To address this, recent approaches apply contrastive decoding to calibrate the model's response via contrasting output distributions with original and visually distorted samples, demonstrating promising hallucination mitigation in a training-free manner. However, the potential of changing information in visual inputs is not well-explored, so a deeper investigation into the behaviors of visual contrastive decoding is of great interest. In this paper, we first explore various methods for contrastive decoding to change visual contents, including image downsampling and editing. Downsampling images reduces the detailed textual information while editing yields new contents in images, providing new aspects as visual contrastive samples. To further study benefits by using different contrastive samples, we analyze probability-level metrics, including entropy and distribution distance. Interestingly, the effect of these samples in mitigating hallucinations varies a lot across LVLMs and benchmarks. Based on our analysis, we propose a simple yet effective method to combine contrastive samples, offering a practical solution for applying contrastive decoding across various scenarios. Extensive experiments are conducted to validate the proposed fusion method among different benchmarks.
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