Using OCR Heads to Verbalize Image Semantics
TL;DR
Across four models, attention heads causally necessary for OCR are identified, and it is discovered that these are in fact general-purpose heads that output interpretable semantic features across all image tokens.
Abstract
How do VLMs map from pixels to semantics? To understand this general question, we focus on a narrow one: studying how VLMs perform optical character recognition (OCR). Across four models, we identify attention heads causally necessary for OCR, and discover that these are in fact general-purpose heads that output interpretable semantic features across all image tokens. For example, pointing these heads at an image token containing the word"bike"causes Qwen3-VL-8B to output"bike,"but pointing them at a bird wing causes the model to output the token"feathers."We collapse these heads'attention weights into a single verbalization lens transformation that reveals interpretable semantic features in hidden states across all layers. When combined with projection to vocabulary space, we can obtain interpretable labels starting from layer 0, showing that image representations are in fact aligned with language in early layers. We find that we can also use the inverse of this transformation to edit non-word concepts, e.g., replacing a tractor with a revolver in a naturalistic image, providing causal evidence that this subspace is useful for more than just OCR. Our results are an example of how the study of specific mechanisms can shed light on broader interpretability problems.
