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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

Text enhancement framework based on open-source large language models

Huo Haoxin1
Guan Weili2
Fang Zhijie3
1. School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
2. College of Digital Economics, Nanning University, Nanning 530299, China
3. School of Electronic Engineering, Guangxi University of Science and Technology, Liuzhou Guangxi 545006, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0235

Abstract

Traditional data augmentation methods in text classification are limited to the data level and often fail to overcome the closed semantic space. To address this issue, this paper proposes a text enhancement framework based on DeepSeek (DS-Aug) . The framework employs a Chain-of-Thought prompt template to generate semantically consistent and domain-specific augmented samples and distilled knowledge. A knowledge-aware transfer fusion model is further designed, in which external knowledge with domain relevance constraints is dynamically injected through an attention gating mechanism. This design improves knowledge transfer and few-shot learning capabilities. Experiments conducted on two public datasets, BBC News topic classification and Movie Review sentiment analysis, demonstrate that DS-Aug achieves higher accuracy and F1-scores compared with traditional augmentation strategies and several pretrained models. The results confirm the effectiveness of DS-Aug in enhancing classification performance, particularly under low-resource and cross-domain scenarios. This study provides a new perspective for applying open-source large language models in domain-specific text classification.

Foundation Support

国家自然科学基金资助项目(12364011)
广西科技计划资助项目(25069027)
中央引导地方科技发展专项资金资助项目(2023PRJ1013)
柳州市科技计划资助项目(2024AA0204A001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0235
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-25] Accepted Paper

Cite This Article

霍浩鑫, 管卫利, 方志杰. 一种基于开源大语言模型的文本增强框架 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0235. (Huo Haoxin, Guan Weili, Fang Zhijie. Text enhancement framework based on open-source large language models [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0235. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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