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.

Text-to-SQL aims to convert natural language questions into executable SQL queries.While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples are unavailable.To overcome this limitation, we propose Self-Augmentation incontext learning with Fine-grained Example selection for Text-to-SQL (SAFE-SQL), a novel unsupervised framework that enhances SQL generation by generating and intelligently filtering self-augmented examples.SAFE-SQL leverages an LLM to generate diverse Textto-SQL examples, which are then filtered by a novel fine-grained mechanism using criteria for semantic similarity, structural alignment, and reasoning path quality to curate highquality in-context learning examples.Leveraging these carefully selected self-generated examples, SAFE-SQL significantly surpasses previous zero-shot and few-shot Text-to-SQL frameworks, achieving superior execution accuracy.Notably, our approach demonstrates substantial performance gains in challenging extra hard and unseen scenarios, where conventional methods often struggle.