Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image. A natural question arises from this phenomenon: Which component of the LVLM pipeline primarily contributes to object hallucinations? The vision encoder to perceive visual information, or the language decoder to generate text responses? In this work, we strive to answer this question through designing a systematic experiment to analyze the roles of the vision encoder and the language decoder in hallucination generation. Our observations reveal that object hallucinations are predominantly associated with the strong priors from the language decoder. Based on this finding, we propose a simple and training-free framework, No-Language-Hallucination Decoding, NoLan, which refines the output distribution by dynamically suppressing language priors, modulated based on the output distribution difference between multimodal and text-only inputs. Experimental results demonstrate that NoLan effectively reduces object hallucinations across various LVLMs on different tasks. For instance, NoLan achieves substantial improvements on POPE, enhancing the accuracy of LLaVA-1.5 7B and Qwen-VL 7B by up to 6.45 and 7.21, respectively. The code is publicly available at: https://github.com/lingfengren/NoLan.

翻译：物体幻觉是大型视觉语言模型（LVLM）中的一个关键问题，表现为输出中包含输入图像中未出现的物体。这一现象引出了一个自然问题：LVLM流程中的哪个组件是导致物体幻觉的主要原因？是用于感知视觉信息的视觉编码器，还是用于生成文本响应的语言解码器？在本工作中，我们通过设计系统性实验来分析视觉编码器和语言解码器在幻觉生成中的作用，以回答此问题。我们的观察表明，物体幻觉主要与语言解码器的强先验相关联。基于这一发现，我们提出了一个简单且无需训练的框架——无语言幻觉解码（No-Language-Hallucination Decoding, NoLan）。该框架通过动态抑制语言先验来优化输出分布，其调节机制基于多模态输入与纯文本输入之间的输出分布差异。实验结果表明，NoLan在不同任务和多种LVLM上均能有效减少物体幻觉。例如，在POPE基准测试中，NoLan实现了显著提升，将LLaVA-1.5 7B和Qwen-VL 7B的准确率分别提高了高达6.45和7.21分。代码公开于：https://github.com/lingfengren/NoLan。