自然语言处理中关于提示学习的研究进展

Research Progress on Prompt Learning in Natural Language Processing

预训练语言模型的出现使处理自然语言处理任务模式发生了巨大变化,对预先训练好的模型进行微调以适应下游任务成为目前自然语言处理任务的主流模式。随着预训练模型越来越大,需要找到轻量化的替代全模型的微调方法,基于提示学习的微调方法可以满足这一需求。对提示学习的研究进展进行总结,先描述了预训练语言模型与提示学习的关系,说明现在寻找替代传统微调方法的必要性,然后详细解释了基于提示学习微调模型的步骤,包括对提示模板的构建、答案搜索和答案映射,再对提示学习在自然语言处理领域的应用举例,最后对提示学习面临的挑战和未来可能的研究方向进行展望,以期对自然语言处理、预训练语言模型和提示学习相关领域的研究提供参考借鉴。

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.