The paper provides a comprehensive survey of recent advancements in Natural Language Processing (NLP) using large pre-trained language models (PLMs). It highlights three main paradigms: pre-training then fine-tuning, prompt-based learning, and NLP as text generation. The authors discuss the evolution of PLMs, from early models like ELMo and ULMFiT to more advanced models like BERT, GPT, and T5. They detail the different types of PLMs, including autoregressive, masked, and encoder-decoder models, and their training objectives. The paper also explores various fine-tuning strategies, such as freezing the PLM for context-sensitive word embeddings, fine-tuning the entire PLM, and using adapter modules for efficient fine-tuning. Additionally, it covers prompt-based learning, including learning from instructions and demonstrations, template-based learning, and learning from proxy tasks. The authors discuss the advantages of prompt-based learning, such as reduced computational requirements and better alignment with pre-training objectives. Finally, the paper concludes with discussions on limitations and future research directions.The paper provides a comprehensive survey of recent advancements in Natural Language Processing (NLP) using large pre-trained language models (PLMs). It highlights three main paradigms: pre-training then fine-tuning, prompt-based learning, and NLP as text generation. The authors discuss the evolution of PLMs, from early models like ELMo and ULMFiT to more advanced models like BERT, GPT, and T5. They detail the different types of PLMs, including autoregressive, masked, and encoder-decoder models, and their training objectives. The paper also explores various fine-tuning strategies, such as freezing the PLM for context-sensitive word embeddings, fine-tuning the entire PLM, and using adapter modules for efficient fine-tuning. Additionally, it covers prompt-based learning, including learning from instructions and demonstrations, template-based learning, and learning from proxy tasks. The authors discuss the advantages of prompt-based learning, such as reduced computational requirements and better alignment with pre-training objectives. Finally, the paper concludes with discussions on limitations and future research directions.