Token Interaction

Token interaction research focuses on optimizing how individual elements (tokens) within sequences, such as words in text or bases in DNA, influence each other during processing. Current efforts concentrate on improving efficiency and accuracy of these interactions, exploring novel architectures like MLP-like models with triangular mixers and attention mechanisms that reduce computational complexity from quadratic to linear. These advancements are crucial for handling long sequences in various applications, including natural language processing, genomics, and recommendation systems, leading to improved model performance and reduced computational costs.