Token Sampling

Token sampling techniques aim to improve the efficiency and interpretability of large language models (LLMs) and other deep learning architectures, such as vision transformers, by selectively processing subsets of input tokens. Current research focuses on developing efficient sampling strategies, including dynamic multi-token prediction, downsampling methods within U-shaped architectures, and object-guided selection, often integrated into models like BERT and Stable Diffusion. These advancements offer significant potential for reducing computational costs in large models while maintaining or even improving performance, leading to more accessible and interpretable AI systems across various applications.