Token Compression

Token compression aims to accelerate and reduce the cost of large language models (LLMs) and other transformer-based architectures by reducing the number of input tokens processed. Current research focuses on developing efficient compression algorithms, including those based on sentence-level encoding, token pruning, and merging, often integrated with attention mechanisms to prioritize important information. These techniques are being applied across various domains, such as question answering, 3D object detection, and multimodal document understanding, demonstrating significant improvements in inference speed and resource efficiency while maintaining comparable performance. The resulting advancements have substantial implications for deploying these computationally intensive models in resource-constrained environments and scaling them to handle increasingly large inputs.