A Survey of Pretrained Language Models for NLP Tasks

Abstract

Pretrained language models (PLMs) have revolutionized natural language processing (NLP) by significantly improving performance across a wide range of tasks. This survey presents an overview of the evolution of PLMs, from traditional approaches to the more advanced transformer-based models. We discuss the key components, training strategies, and major models, such as BERT, GPT, T5, and their variants. Furthermore, we explore the applications of PLMs in various NLP tasks, including text classification, question answering, machine translation, and named entity recognition. Additionally, we highlight challenges in fine-tuning, model efficiency, interpretability, and domain adaptation. The paper concludes by outlining the future directions of PLMs, emphasizing the need for more efficient, robust, and domain-specific models.

Keywords: Pretrained Language Models, NLP, BERT, GPT, T5, Transformer, Fine-Tuning, Model Efficiency, Interpretability, Domain Adaptation