Interleaved Image Text Generation

Interleaved image-text generation focuses on creating models that seamlessly alternate between generating images and text, following a given prompt or instruction. Current research emphasizes developing efficient and effective model architectures, often adapting large language and vision models through techniques like parameter-efficient fine-tuning and modality-specific adaptations to improve instruction following and overall coherence. This area is significant because it advances multimodal generation capabilities, leading to improved applications in areas such as storytelling, interactive tutorials, and dynamic content creation, while also driving the development of more robust evaluation methods for this complex task.