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OA

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.