超越表象：通过语义锚定测量视觉语言模型中的象征性差距
More Than Meets the Eye: Measuring the Semiotic Gap in Vision-Language Models via Semantic Anchorage
AI总结：
研究通过引入DIVA基准测试，探讨高视觉保真度是否干扰视觉抽象中的隐喻构成性，提出语义对齐差距和方向偏置指标，发现模型规模无法解决字面偏好，高保真度导致象征性对齐减弱。
摘要：
视觉语言模型（VLMs）在逼真生成方面表现出色，但往往难以表示抽象意义，如名词短语的隐喻解释。为研究高视觉保真度是否干扰隐喻构成性，我们引入DIVA基准测试，通过生成字面和隐喻解读的配对意义锚定可视化来替代高保真细节。我们进一步提出语义对齐差距（Δ），一种架构无关的度量标准，用于量化字面与隐喻视觉基础之间的差异。我们还引入方向性符号偏置b(t)以分别测量字面偏好的方向和强度。评估8种近期VLMs后，我们发现一致的字面优越偏置：模型规模本身无法解决字面偏好，增加视觉保真度与象征性对齐减弱相关，表明超现实图像可能产生认知干扰。我们的发现表明，提升构成性理解需要对视觉输入进行图标抽象，并将解释和生成锚定在预期意义。
英文摘要：
Vision-Language Models (VLMs) excel at photorealistic generation, yet often struggle to represent abstract meaning such as idiomatic interpretations of noun compounds. To study whether high visual fidelity interferes with idiomatic compositionality under visual abstraction, we introduce DIVA, a controlled benchmark that replaces high-fidelity visual detail with schematic iconicity by generating paired, sense-anchored visualizations for literal and idiomatic readings. We further propose Semantic Alignment Gap ($Δ$), an architecture-agnostic metric that quantifies divergence between literal and idiomatic visual grounding. We additionally introduce a directional signed bias $b(t)$ to separately measure the direction and strength of literal preference. Evaluating 8 recent VLMs, we reveal a consistent Literal Superiority Bias: model scale alone does not resolve literal preference, and increased visual fidelity is associated with weaker symbolic alignment, suggesting cognitive interference from hyper-realistic imagery. Our findings suggest that improving compositional understanding requires iconographic abstraction of visual input and anchoring interpretation and generation in intended meaning.
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