Shot Intent Classification

Shot intent classification focuses on accurately identifying the user's intention from limited training data, a crucial task in building robust conversational AI systems. Current research emphasizes leveraging large language models (LLMs), often through techniques like parameter-efficient fine-tuning or instruction-tuning, and explores various architectures including cross-encoders and bi-encoders with different similarity functions and training regimes (e.g., episodic meta-learning). These advancements aim to improve the efficiency and accuracy of intent classification, particularly in low-resource scenarios where labeled data is scarce, thereby enabling faster development and deployment of more adaptable dialogue systems.