Linear Attention Model

Linear attention models aim to address the quadratic complexity bottleneck of traditional transformer-based models, enabling efficient processing of long sequences in natural language processing tasks. Current research focuses on developing and optimizing various linear attention architectures, such as those incorporating data-dependent or -independent decay mechanisms, and integrating them with techniques like speculative decoding and sequence parallelism for improved speed and scalability. These advancements significantly enhance the efficiency of large language models, allowing for faster training and inference on longer sequences while maintaining or even improving performance on downstream tasks like machine translation and question answering.