Zero Shot Prompting

Zero-shot prompting aims to leverage large language models (LLMs) for tasks without any task-specific training data, relying solely on carefully crafted prompts. Current research focuses on improving prompt design, including techniques like chain-of-thought prompting and instance-adaptive prompting, to enhance reasoning capabilities across diverse tasks such as commonsense reasoning, question answering, and even visual-spatial reasoning. This approach offers a cost-effective and efficient way to adapt LLMs to new tasks, impacting fields like knowledge graph engineering and systematic review acceleration by potentially automating parts of these processes.