与知识相结合的提示学习研究综述  

A Survey of Prompt Learning Combined with Knowledge

作　　者：鲍琛龙 吕明阳 唐晋韬[1] 李莎莎[1] 王挺[1] BAO Chenlong;LYU Mingyang;TANG Jintao;LI Shasha;WANG Ting(College of Computer Science and Technology,National University of Defense Technology,Changsha,Hunan 410073,China)

机构地区：[1]国防科技大学计算机学院,湖南长沙410073

出　　处：《中文信息学报》2023年第7期1-12,共12页Journal of Chinese Information Processing

基　　金：科技创新2030-“新一代人工智能”重大项目(2021ZD0110700);湖南省自然科学基金(2022JJ30668)。

摘　　要：近年来,提示学习方法由于可以充分激发预训练语言模型的潜能而得到了研究者越来越多的关注,特别是在知识抽取任务中取得了较好进展。为了提升提示学习性能,研究者也开展了基于知识的提示学习模板工程、答案工程优化等多项研究。该文对提示学习与知识相结合的相关研究进行了系统综述,包括知识抽取中的提示学习方法以及基于知识约束的提示学习相关进展。在此基础上,该文还探讨了目前方法存在的局限性,展望了提示学习与知识相结合的发展趋势。In recent years,prompt learning methods have attracted more attention from researchers for exploiting the pre-trained language models.To optimize the performance of prompt learning,researchers explored the template engineering and the answer engineering which are both based on knowledge.In this paper,the researches on prompt learning combined with knowledge are reviewed systematically,with a focus on the prompt learning methods in knowledge extraction.We also reveal the constraints in these method and discuss the possible future developments.

关 键 词：提示学习 知识抽取 知识提示 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]