AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
It serves as a reference for researchers and practitioners to learn about and stay updated on T-PTLMs in NLP, but it is incremental as it synthesizes existing knowledge without introducing novel methods.
This survey paper provides a comprehensive overview of transformer-based pretrained language models (T-PTLMs) in NLP, covering their evolution, core concepts, taxonomy, benchmarks, and future directions, without presenting new experimental results or numbers.
Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT. These models are built on the top of transformers, self-supervised learning and transfer learning. Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks. These models provide good background knowledge to downstream tasks which avoids training of downstream models from scratch. In this comprehensive survey paper, we initially give a brief overview of self-supervised learning. Next, we explain various core concepts like pretraining, pretraining methods, pretraining tasks, embeddings and downstream adaptation methods. Next, we present a new taxonomy of T-PTLMs and then give brief overview of various benchmarks including both intrinsic and extrinsic. We present a summary of various useful libraries to work with T-PTLMs. Finally, we highlight some of the future research directions which will further improve these models. We strongly believe that this comprehensive survey paper will serve as a good reference to learn the core concepts as well as to stay updated with the recent happenings in T-PTLMs.