Vision-Language Models (VLMs) have gained community-spanning prominence due
to their ability to integrate visual and textual inputs to perform complex
tasks. Despite their success, the internal decision-making processes of these
models remain opaque, posing challenges in high-stakes applications. To address
this, we introduce NOTICE, the first Noise-free Text-Image Corruption and
Evaluation pipeline for mechanistic interpretability in VLMs. NOTICE
incorporates a Semantic Minimal Pairs (SMP) framework for image corruption and
Symmetric Token Replacement (STR) for text. This approach enables semantically
meaningful causal mediation analysis for both modalities, providing a robust
method for analyzing multimodal integration within models like BLIP. Our
experiments on the SVO-Probes, MIT-States, and Facial Expression Recognition
datasets reveal crucial insights into VLM decision-making, identifying the
significant role of middle-layer cross-attention heads. Further, we uncover a
set of ``universal cross-attention heads'' that consistently contribute across
tasks and modalities, each performing distinct functions such as implicit image
segmentation, object inhibition, and outlier inhibition. This work paves the
way for more transparent and interpretable multimodal systems.

通过在视觉语言模型中引入 NOTICE 技术，实现了对其决策过程的透明化和可解释性，从而揭示了关键的决策因素和多模态整合中的中间层交叉注意力。