中图分类号：TP391.1
文献标识码：ADOI:10.19358/j.issn.2097-1788.2023.12.002
引用格式：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.

Large language model based on sentence granularity prompts for temporal knowledge Q&A approach

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 research 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 temporal knowledge Q&A for large language models based on sentence granularity prompts. Firstly, by improving the sentence granularity 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 experiments 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.

Key words :temporal knowledge graph questionanswering; large language models; prompt learning；natural language processing