信息通信技术与政策2026，Vol.52Issue(5)：41-49,9.DOI:10.12267/j.issn.2096-5931.2026.05.006
面向大模型训练的通信行业高质量数据集构建方法与实践
Construction methods and practice of high-quality datasets for telecommunications large model training
肖文彬 1李雨霏 2黄倚霄 1马闻达2
作者信息
- 1. 中国移动通信集团广东有限公司,广州 510150
- 2. 中国信息通信研究院人工智能研究所,北京 100191
- 折叠
摘要
Abstract
With the rapid evolution of generative artificial intelligence technology,data quality has become the core bottleneck restricting the performance of industry-scale large language models.Telecom operators possess ZB-scale cross-domain data,providing inherent resource advantages for training vertical large language models.However,raw communications data generally faces issues such as multi-source heterogeneity,high redundancy,and scarcity of long-tail samples,which limits its effectiveness when directly applied to model training.To address this,the system proposes a high-quality dataset construction method encompassing"collection-governance-annotation-evaluation,"featuring key technologies such as deep semantic compression,long-tail data synthesis based on Wasserstein Generative Adversarial Network(WGAN)and Long Short-Term Memory(LSTM)network,domain ontology construction,and human-machine collaborative annotation.Meanwhile,a general and specialized knowledge data coordination mechanism is designed to effectively mitigate catastrophic forgetting during industry fine-tuning.Practice has proved that this method is effective and can provide reference for the construction of high-quality datasets in the telecommunications industry.关键词
高质量数据集/通信行业/数据合成/领域本体/数据增强Key words
high-quality dataset/telecommunications industry/data synthesis/domain ontology/data augmentation分类
管理科学引用本文复制引用
肖文彬,李雨霏,黄倚霄,马闻达..面向大模型训练的通信行业高质量数据集构建方法与实践[J].信息通信技术与政策,2026,52(5):41-49,9.
