External Keyword Matrix

External keyword matrices are being explored as a method to enhance the efficiency and accuracy of various machine learning models, particularly in large-scale applications like language modeling and customer service platforms. Current research focuses on integrating these matrices with existing architectures, such as transformers, often using low-rank approximations and adaptive methods to manage computational costs and improve model performance. This work addresses challenges like parameter efficiency, memory limitations during training, and the need for improved accuracy in handling complex data, ultimately aiming to improve the scalability and effectiveness of machine learning systems.