How do you measure the interpretability of Vision-Language Models?

Measuring the interpretability of Vision-Language Models (VLMs) involves evaluating how well humans can understand the reasoning behind their predictions. This typically combines techniques that visualize model behavior, assess alignment between modalities, and quantify consistency in decision-making. Since VLMs process both images and text, interpretability focuses on tracing how visual and textual features interact to produce outputs like captions or answers.

One approach is analyzing attention mechanisms or saliency maps to identify which parts of an image or text the model prioritizes. For example, tools like Grad-CAM highlight image regions influencing a caption, such as a dog’s face when generating “a brown dog.” Similarly, attention layers in models like CLIP can show how specific words correlate with visual patches. Probing tasks also help: testing if a model correctly answers “What color is the car?” after masking irrelevant image regions verifies if it uses logical visual cues. These methods reveal whether the model’s focus aligns with human intuition, a key aspect of interpretability.

Another layer involves evaluating cross-modal consistency. Human evaluation is often used here, where annotators rate how well explanations (e.g., “the cat is on the couch”) match the image. Perturbation tests further validate this: altering an image (e.g., removing the couch) and checking if the model’s output changes appropriately. For instance, if a VLM still mentions the couch after it’s edited out, this signals flawed reasoning. Tools like LIME or SHAP can approximate which features drive decisions, helping developers spot inconsistencies between visual and textual reasoning paths.

Finally, quantitative metrics provide objective benchmarks. Pointing game accuracy measures how often a model’s highlighted image region matches the described object (e.g., correctly locating “apples” in a basket). Task-specific benchmarks like VQA accuracy (for question answering) or CIDEr scores (for captioning) indirectly reflect interpretability by testing if outputs make sense contextually. However, these should complement—not replace—qualitative analysis. For example, a high VQA score doesn’t guarantee the model uses logical reasoning, so combining metrics with visual checks ensures a holistic assessment of interpretability.