Multitask Retrieval

Multitask retrieval aims to build single information retrieval systems capable of handling diverse query types and modalities (text, images, etc.), improving efficiency and reducing resource needs compared to task-specific approaches. Current research focuses on developing unified models trained across multiple datasets and tasks, often leveraging large language models and instruction-tuning to enhance generalization and performance. This approach promises to significantly advance multimodal information access, enabling more efficient and versatile search engines and knowledge-based systems across various applications.