generalized few-shot intent detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm,
Attention-aware Self-adaptive Prompt (ASP) is a novel framework for Few-Shot Class-Incremental Learning (FSCIL), which prevents overfitting on base tasks and outperforms state-of-the-art methods in learning new classes and mitigating forgetting.