发现并解决视觉语言模型中跨模态知识冲突问题
Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models
- 提出系统性方法检测视觉与文本回答间的知识冲突
- 在多个模型上发现高比例冲突，且不随规模变化
- 通过动态解码和提示策略提升模型准确率
大型视觉语言模型（LVLMs）在处理多模态输入时表现出强大能力，但其视觉与语言组件间存在参数化知识冲突，即知识表示不一致。本文首次形式化定义了跨模态参数知识冲突问题，并提出一套检测、解释与缓解的系统方法。构建了一个识别视觉与文本答案冲突的流水线，发现近期主流LVLM在不同规模下均存在持续高冲突率。进一步分析冲突对推理过程的影响，提出对比度量以区分冲突样本。基于此，开发一种动态对比解码方法，根据答案置信度剔除来自信心较低模态的无效logits。针对不提供logits的模型，提出两种基于提示的缓解策略。在ViQuAE和InfoSeek数据集上验证效果，使用LLaVA-34B时平均准确率提升2.24%。
原文摘要 · Abstract (English)
Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs. However, these models are prone to parametric knowledge conflicts, which arise from inconsistencies of represented knowledge between their vision and language components. In this paper, we formally define the problem of $\textbf{cross-modality parametric knowledge conflict}$ and present a systematic approach to detect, interpret, and mitigate them. We introduce a pipeline that identifies conflicts between visual and textual answers, showing a persistently high conflict rate across modalities in recent LVLMs regardless of the model size. We further investigate how these conflicts interfere with the inference process and propose a contrastive metric to discern the conflicting samples from the others. Building on these insights, we develop a novel dynamic contrastive decoding method that removes undesirable logits inferred from the less confident modality components based on answer confidence. For models that do not provide logits, we also introduce two prompt-based strategies to mitigate the conflicts. Our methods achieve promising improvements in accuracy on both the ViQuAE and InfoSeek datasets. Specifically, using LLaVA-34B, our proposed dynamic contrastive decoding improves an average accuracy of 2.24%.
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