Extraction Model

Extraction models aim to automatically identify and extract specific information from various data sources, such as text, images, and documents, with applications ranging from knowledge graph construction to clinical text analysis. Current research focuses on improving model accuracy and efficiency across diverse data types and formats, employing techniques like reinforcement learning, large language models (LLMs), and hybrid approaches combining LLMs with smaller, specialized models to address challenges like long-range dependencies and noisy data. These advancements have significant implications for various fields, enabling more efficient knowledge acquisition, improved information retrieval, and automated data processing in diverse applications.