现代情报2025，Vol.45Issue(4)：36-48,13.DOI:10.3969/j.issn.1008-0821.2025.04.004
大语言模型在摘要结构功能识别上的应用研究
Research on the Application of Large Language Models in Sentence Function Recognition of Abstracts
摘要
Abstract
[Purpose/Significance]The study aims to test the feasibility and application potential of GPT in the task of identifying the sentence function categories of abstracts,providing a reference for building high-quality structured data based on generative large language models.[Method/Process]The paper used single-round dialogue and zero-shot prompts based on GPT 4.0,Qwen 1.5 and ERNIE 4.0 to perform the category identification task of structure and function.Different test subsets were constructed according to domain,language and time range of the publication.Then,the P,R and F1 values,and accuracy were used as evaluation indicators.And the single-factor analysis of variance was used to measure the different performance between subsets.[Results/Conclusion]The outputs of large language model exceeded the categories restriction in the prompts.However,the high proportion of outputs that meet the prompts shows that using generative models to solve discriminative tasks is basically feasible.Different large language models have different perform-ances.Some indicators of GPT 4.0 and ENRIE 1.5 are significant at the 0.01-level,while others are not.The indicators include:all indicators of the samples in different categories of structural function,the R and accuracy of samples in differ-ent fields,and the P and the F1 value of samples in different languages.In the future,when building intelligent intelli-gence services based on generative large language models,we should focus on the controllability of output results,domain adaptability,etc.关键词
结构功能识别/生成式大模型/大语言模型/结构化摘要/语步识别Key words
structural function recognition/large language model/generative large language models/structured ab-stract/move recognition分类
信息技术与安全科学引用本文复制引用
翁梦娟,王晓光,桂恒,刘文斌,石佛波..大语言模型在摘要结构功能识别上的应用研究[J].现代情报,2025,45(4):36-48,13.基金项目
国家社会科学基金重大项目"文化遗产智慧数据资源建设与服务研究"(项目编号:21&ZD334). （项目编号:21&ZD334）
