Generative Semantic Probing for Vision-Language Models via Hierarchical Feature Optimization

Vision-language models (VLMs) has demonstrated impressive cross-modal alignment. However, their internal mechanisms of associating text concepts with visual patterns remain opaque. This opacity raises a critical question: What visual patterns do VLMs inherently associate with text concepts? Current methods for decoding representations of VLMs often produce suboptimal outputs, hindering to probe the clear visual patterns. To address this, we introduce Generative Semantic Probing (GSP), a novel training-free framework that synthesizes images to probe the implicit semantic preferences of VLMs. Our method generates visual patterns that maximize the similarity to the target text embeddings, through three core components: (1) Hierarchical Feature Decomposition, which decomposes the image generation across multi-scale feature levels; (2) Feature Space Constraint, which constrains the optimization within semantically meaningful feature subspace; (3) Quality Assessment Module, which ensures the generation of visually plausible outputs. Experiments validate our method's strengths in high-fidelity image generation and interpretable model analysis. Beyond text-to-image generation, style transfer and image editing applications, our framework enables unprecedented visualization of VLMs' decision boundaries. By exposing implicit preferences and systematic biases in the cross-modal association, our work provides a valuable insight for both understanding and improvement of the vision-language alignment.