Token Sparsification

Token sparsification aims to improve the efficiency of large language and vision models by selectively reducing the number of tokens processed during inference, thereby decreasing computational cost and memory usage without significant accuracy loss. Current research focuses on developing efficient, often training-free, methods for identifying and pruning less important tokens, exploring both token-level and channel-level sparsity, and optimizing the allocation of computational resources across different layers of the model. These advancements are significant because they enable the deployment of larger and more powerful models on resource-constrained devices, accelerating inference speed and expanding the practical applications of these technologies in various domains.