中圖分類號：TP391.1
文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2023.12.002
引用格式：李志東，羅琪彬，喬思龍.基于句粒度提示的大語言模型時序知識問答方法［J］.網(wǎng)絡安全與數(shù)據(jù)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