数字图书馆论坛2026，Vol.22Issue(4)：46-55,10.DOI:10.3772/j.issn.1673-2286.2026.04.005
基于大语言模型与对比学习的文本可读性自动分级方法研究
Research on Automatic Text Readability Grading Method Based on Large Language Model and Contrastive Learning
摘要
Abstract
Text readability greatly affects users'information understanding,absorption,and use.To address the problems of confusion between adjacent readability levels and insufficient high-quality labeled data in Chinese text readability grading,this paper proposes an automatic readability grading method based on large language models and contrastive learning.First,a large language model is used to generate contrastive samples with clear readability gradients to expand the training data.Then,BERT is employed to extract deep semantic features,and contrastive learning is introduced to optimize level boundaries in the feature space,thereby improving the model's ability to distinguish adjacent levels.The experimental results demonstrate that the accuracy(0.880)and F1-score(0.881)of the proposed model outperform all baseline models,suggesting that this method can effectively enhance the performance of automatic readability level classification for Chinese texts,particularly showing a clear advantage in discriminating adjacent-level texts.关键词
大语言模型/对比学习/可读性/文本信息/自动分级Key words
Large Language Model/Contrastive Learning/Readability/Text Information/Automatic Classification分类
信息技术与安全科学引用本文复制引用
储伊力,曹振祥,姜烨,兰新苗..基于大语言模型与对比学习的文本可读性自动分级方法研究[J].数字图书馆论坛,2026,22(4):46-55,10.基金项目
本研究得到安徽省哲学社会科学规划项目"安徽省老年人健康信息文本可读性评估研究"(编号:AHSKQ2022D141)资助. （编号:AHSKQ2022D141）
