Dynamic Token

Dynamic token pruning is a technique aimed at improving the efficiency of large language models (LLMs) and vision transformers (ViTs) by selectively processing only the most relevant input tokens. Current research focuses on developing adaptive algorithms that dynamically determine which tokens to prune based on factors like attention scores or semantic importance, often within the context of specific model architectures such as LLMs, ViTs, and multimodal models. This approach offers significant potential for reducing computational costs and accelerating inference without substantial accuracy loss, impacting both the speed and scalability of various AI applications. The resulting efficiency gains are particularly relevant for resource-constrained environments and applications requiring real-time processing.