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提出了一种基于LLM的Teacher-Student弱监督框架，用于网络安全推文分类，在无需大规模人工标注的情况下训练的PLM模型F1超过0.92，接近完全人工监督的性能。
This paper proposes a modular approach for adapting pretrained language models to low-resource languages by freezing embeddings and tuning the rest, showing improvements on NLU tasks for Scottish Gaelic, Irish, and Quechua.