title: How Do Large Vision-Language Models See Text in Image? Unveiling the
    Distinctive Role of OCR Heads
summary: Despite significant advancements in Large Vision Language Models (LVLMs), a
  gap remains, particularly regarding their interpretability and how they locate
  and interpret textual information within images. In this paper, we explore
  various LVLMs to identify the specific heads responsible for recognizing text
  from images, which we term the Optical Character Recognition Head (OCR Head).
  Our findings regarding these heads are as follows: (1) Less Sparse: Unlike
  previous retrieval heads, a large number of heads are activated to extract
  textual information from images. (2) Qualitatively Distinct: OCR heads possess
  properties that differ significantly from general retrieval heads, exhibiting
  low similarity in their characteristics. (3) Statically Activated: The
  frequency of activation for these heads closely aligns with their OCR scores.
  We validate our findings in downstream tasks by applying Chain-of-Thought (CoT)
  to both OCR and conventional retrieval heads and by masking these heads. We
  also demonstrate that redistributing sink-token values within the OCR heads
  improves performance. These insights provide a deeper understanding of the
  internal mechanisms LVLMs employ in processing embedded textual information in
  images.
paper_authors._id: 68301a21694f7a58a32919a8
paper_authors.name: Ingeol Baek
paper_authors._id: 68301a21694f7a58a32919a9
paper_authors.name: Hwan Chang
paper_authors._id: 68301a21694f7a58a32919aa
paper_authors.name: Sunghyun Ryu
paper_authors._id: 68301a21694f7a58a32919ab
paper_authors.name: Hwanhee Lee
paper_title: How Do Large Vision-Language Models See Text in Image? Unveiling the
    Distinctive Role of OCR Heads
paper_summary: Despite significant advancements in Large Vision Language Models (LVLMs), a
  gap remains, particularly regarding their interpretability and how they locate
  and interpret textual information within images. In this paper, we explore
  various LVLMs to identify the specific heads responsible for recognizing text
  from images, which we term the Optical Character Recognition Head (OCR Head).
  Our findings regarding these heads are as follows: (1) Less Sparse: Unlike
  previous retrieval heads, a large number of heads are activated to extract
  textual information from images. (2) Qualitatively Distinct: OCR heads possess
  properties that differ significantly from general retrieval heads, exhibiting
  low similarity in their characteristics. (3) Statically Activated: The
  frequency of activation for these heads closely aligns with their OCR scores.
  We validate our findings in downstream tasks by applying Chain-of-Thought (CoT)
  to both OCR and conventional retrieval heads and by masking these heads. We
  also demonstrate that redistributing sink-token values within the OCR heads
  improves performance. These insights provide a deeper understanding of the
  internal mechanisms LVLMs employ in processing embedded textual information in
  images.
paper_ai_summary: The study identifies and analyzes OCR Heads within Large Vision Language Models, revealing their unique activation patterns and roles in interpreting text within images.
paper_ai_keywords: Large Vision Language Models LVLMs Optical Character Recognition Head OCR Head retrieval heads Chain-of-Thought CoT sink-token values