网络安全与数据治理2023，Vol.42Issue(12)：7-13,7.DOI:10.19358/j.issn.2097-1788.2023.12.002
基于句粒度提示的大语言模型时序知识问答方法
Large language model based on sentence granularity prompts for temporal knowledge Q&A approach
摘要
Abstract
Knowledge Q&A is one of the hot research topics in the field of natural language processing,and temporal knowledge Q&A is a difficult area of Q&A reasoning because it also needs to consider the temporal relationship of knowledge.Today's re-search usually focuses on the word vector similarity between knowledge and questions as an important basis for answering,while ignoring the sentence granularity semantic information embedded in the knowledge.In this paper,we propose a method of tempo-ral knowledge Q&A for large language models based on sentence granularity prompts.Firstly,by improving the sentence granulari-ty prompts,the large language models can learn the sentence granularity semantic information efficiently,and then the temporal knowledge Q&A ability of large language models under Zero-shot,Few-shot and weakly-supervised fine-tuning is verified.The ex-periments are conducted on the ICEWS05-15 dataset,and the accuracy of answers is significantly improved,which demonstrates the effectiveness of the temporal knowledge Q&A method for large language models based on sentence granularity prompts.关键词
时序知识问答/大语言模型/提示学习/自然语言处理Key words
temporal knowledge graph question-answering/large language models/prompt learning/natural language process-ing分类
信息技术与安全科学引用本文复制引用
李志东,罗琪彬,乔思龙..基于句粒度提示的大语言模型时序知识问答方法[J].网络安全与数据治理,2023,42(12):7-13,7.基金项目
173基础加强计划(2022-JCJQ-JJ-0935) （2022-JCJQ-JJ-0935）
