Shot Natural Language Understanding

Few-shot natural language understanding (NLU) focuses on enabling language models to perform well on new tasks with minimal training data. Current research emphasizes developing parameter-efficient fine-tuning techniques, such as adapters and low-rank matrix decompositions, and improving prompt engineering through methods like meta-learning and data augmentation strategies guided by label semantics. These advancements aim to reduce the computational cost and data requirements for adapting large language models to diverse NLU tasks, ultimately improving the efficiency and scalability of NLP applications.