通过对比嵌入链理解大规模视觉语言模型的语言先验

摘要

大规模视觉语言模型（LVLMs）在多模态任务上表现出色，然而它们往往依赖于语言先验（LP）——即预训练期间记忆的文本模式，而未能充分利用视觉证据。先前对LP的分析主要依赖于输入输出探测，这种方法无法揭示视觉何时以及如何影响模型行为的内部机制。为填补这一空白，我们首次通过嵌入链的视角对语言先验进行了系统性分析，深入研究了LVLMs内部各层的表示动态。我们的分析揭示了一个普遍现象：每个模型都存在一个视觉整合点（VIP），即视觉信息开始显著重塑隐藏表示并影响解码的关键层。基于这一发现，我们提出了总视觉整合（TVI）估计器，它通过聚合VIP之后的表示距离来量化视觉查询对响应生成的强烈程度。在涵盖9种当代LVLMs和6个基准测试的54个模型-数据集组合中，我们证明了VIP的一致存在，且TVI能可靠预测语言先验的强度。这为诊断和理解LVLMs中的语言先验提供了一个原则性的工具包。

English

Large vision-language models (LVLMs) achieve strong performance on multimodal tasks, yet they often default to their language prior (LP) -- memorized textual patterns from pre-training while under-utilizing visual evidence. Prior analyses of LP mostly rely on input-output probing, which fails to reveal the internal mechanisms governing when and how vision influences model behavior. To address this gap, we present the first systematic analysis of language prior through the lens of chain-of-embedding, which examines the layer-wise representation dynamics within LVLMs. Our analysis reveals a universal phenomenon: each model exhibits a Visual Integration Point (VIP), a critical layer at which visual information begins to meaningfully reshape hidden representations and influence decoding. Building on this observation, we introduce the Total Visual Integration (TVI) estimator, which aggregates representation distance beyond the VIP to quantify how strongly visual query influences response generation. Across 54 model-dataset combinations spanning 9 contemporary LVLMs and 6 benchmarks, we demonstrate that VIP consistently emerges, and that TVI reliably predicts the strength of language prior. This offers a principled toolkit for diagnosing and understanding language prior in LVLMs.

通过对比嵌入链理解大规模视觉语言模型的语言先验

Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding

摘要

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