Token Model

Token models represent a crucial area of research in natural language processing and computer vision, focusing on efficiently encoding information at the token level to improve model performance and reduce computational costs. Current research explores various architectures and training methods, including masked token modeling for self-supervised learning and hybrid inference approaches combining large and small language models to optimize resource utilization. These advancements aim to enhance the efficiency and effectiveness of large language models, leading to improved performance in tasks such as information extraction and natural language understanding while mitigating the high computational demands of these models.