Zero Shot Intent Classification

Zero-shot intent classification aims to categorize user utterances into intents without requiring labeled training data for each intent, addressing the challenge of handling numerous, rapidly evolving intents in applications like voice assistants. Current research focuses on leveraging large language models for data augmentation and zero-shot prediction, employing techniques like meta-learning, multimodal approaches (combining audio and text), and parameter-efficient fine-tuning to improve performance. These advancements are significant for building more robust and scalable natural language understanding systems, particularly in resource-constrained scenarios where obtaining labeled data is expensive or impractical.