Research Progress on Prompt Learning in Natural Language Processing
The emergence of pre-trained language models has greatly changed the way natural language processing tasks are handled.Fine-tuning pre-trained models to adapt to downstream tasks has become the mainstream mode of natural language processing tasks.As pre-training models become larger and larger,it is necessary to find lightweight alternatives to full-model fine-tuning methods.Fine-tuning methods based on prompt learning can meet this demand.This article summarizes the research progress of prompt learning,first describing the relationship between pre-trained language models and prompt learning,explaining the necessity of finding alternatives to traditional fine-tuning methods,and then explaining in detail the steps of fine-tuning models based on prompt learning,including the construction of prompt templates,an-swer search and answer mapping.Then examples of the application of prompt learning in the field of natural language processing are given,and finally an outlook is given on the challenges and possible research directions faced by prompt learning,hoping this helps with research in natural language processing,pre-trained language models and prompt learning related fields.
prompt learningnatural language processingfine-tuning methodspre-trained language modelsdeep learning
万方数据
维普
