Zero Shot Commonsense

Zero-shot commonsense reasoning aims to enable AI models to answer questions requiring common sense knowledge without explicit training on those specific questions. Current research focuses on improving the quality of synthetic question-answering datasets derived from commonsense knowledge graphs, developing novel model architectures that leverage multi-hop reasoning and incorporate diverse knowledge sources, and refining evaluation metrics beyond traditional perplexity measures. These advancements are crucial for building more robust and generalizable AI systems capable of handling real-world scenarios that demand common sense understanding, with applications ranging from question answering to robotic scene understanding.