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A Label-Guided Neural Topic Model (LGNTM) is proposed, which learns dedicated label-aligned topics, grounds them in lexical and document semantic spaces, and preserves consistency between topic structures and label structures.
Language models suffer from adversarial vulnerability, where imperceptible perturbations in inputs can mislead model predictions. Existing adversarial training methods enhance model robustness by augmenting training data with worst-case adversarial perturbations for each input. However, these approaches overlook the...
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