Multi Modal PromPt

Multi-modal prompting leverages the combined power of different data modalities (e.g., text, images, audio, video) to guide large language models (LLMs) and other AI systems towards specific tasks. Current research focuses on optimizing prompt sequencing and design to improve model performance, particularly in complex reasoning and zero-shot learning scenarios, often employing techniques like prompt engineering and adapter modules to integrate diverse input types. This approach is significantly impacting various fields, including autonomous driving, medical image analysis, and human-object interaction detection, by enabling more robust, efficient, and adaptable AI systems. The development of universal frameworks and datasets for multi-modal prompting is also a key area of ongoing investigation.