Decoder Only Model

Decoder-only models, a type of large language model, are becoming increasingly prominent in natural language processing, aiming to improve efficiency and performance compared to traditional encoder-decoder architectures. Current research focuses on understanding their scaling laws, optimizing training strategies (including supernet training and techniques like "pause tokens"), and exploring their application in diverse tasks such as machine translation, speech recognition, and multi-object tracking. These advancements offer potential for more efficient and effective language models, impacting both the development of new algorithms and the practical deployment of NLP applications in resource-constrained environments.