Shot Semantic Parsing

Shot semantic parsing focuses on efficiently mapping natural language into formal representations, such as logical forms or code, using limited training data. Current research emphasizes leveraging large language models (LLMs) within few-shot learning frameworks, often incorporating techniques like meta-learning, logic programming, and interactive feedback mechanisms to improve accuracy and robustness, particularly in handling ambiguous language. This area is crucial for advancing natural language understanding and enabling more efficient development of applications like voice assistants and question-answering systems that can adapt to new tasks and languages with minimal retraining.