Text Comprehension

Text comprehension research focuses on enabling machines to understand and interact with textual information within various contexts, particularly in conjunction with visual data. Current efforts concentrate on improving multimodal models' ability to handle complex, nuanced text-image relationships, employing techniques like Mixture-of-Experts (MoE) architectures and instruction-guided training to enhance both comprehension and generation capabilities. These advancements are crucial for improving applications ranging from image captioning and scene text recognition to more sophisticated tasks like interleaved image-text comprehension and multimodal recommendation systems. The development of robust evaluation metrics is also a key area of ongoing research.