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Large-scale pre-trained vision foundation models, such as CLIP, have becomede facto backbones for various vision tasks. However, due to their black-boxnature, understanding the underlying rules behind these models' predictions andcontrolling model behaviors have remained open challenges. We present aframework for interpreting vision transformer's latent tokens with naturallanguage. Given a latent token, our framework retains its semantic informationto the final layer using transformer's local operations and retrieves theclosest text for explanation. Our approach enables understanding of modelvisual reasoning procedure without needing additional model training or datacollection. Based on the obtained interpretations, our framework allows formodel editing that controls model reasoning behaviors and improves modelrobustness against biases and spurious correlations.

Interpreting and Controlling Vision Foundation Models via Text Explanations