Shallow Decoder

Shallow decoder architectures aim to improve the efficiency of encoder-decoder models, primarily by reducing computational cost and latency during inference, without significantly sacrificing performance. Current research focuses on developing strategies like dynamic early exiting, employing simpler decoder structures (e.g., linear transforms), and using multiple shallow decoders specialized for subsets of tasks or languages. This work is significant because it addresses a critical bottleneck in deploying complex models like those used in machine translation and image compression, enabling faster and more resource-efficient applications.