LLM-Based Weak Annotation for Cybersecurity Tweet Classification: A Teacher-Student Framework

The paper introduces a Teacher-Student framework that uses a large language model (LLM) to generate weak labels for unlabeled cybersecurity tweets, enabling the training of domain‑specific pre‑trained language models (PLMs) without extensive manual annotation. Experiments demonstrate that PLMs trained solely on these LLM‑generated labels achieve F1 scores above 0.92, within 3 percentage points of fully human‑supervised models across all evaluated architectures. This approach offers a practical alternative for creating cybersecurity‑specific training data when expert annotation resources are scarce.

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