Parallel Language Models via Variational Decoding

Researchers have introduced Parallel Language Models, a new framework that enables the generation of multiple tokens in a single forward pass. This method, termed variational decoding, aims to reduce the high inference latency typically associated with autoregressive models. By allowing for simultaneous token output, the technique seeks to improve processing efficiency as language models continue to scale in size and complexity.