Adaptive Token

Adaptive tokenization techniques aim to optimize the efficiency and performance of transformer-based models, particularly in vision and language processing, by dynamically adjusting the number or length of input tokens processed. Current research focuses on developing algorithms and model architectures that selectively retain informative tokens while discarding redundant ones, often leveraging attention mechanisms or psycholinguistic principles to guide this selection process. These advancements promise to improve the speed and resource efficiency of large language and vision-language models, making them more practical for real-world applications with limited computational resources.