Structured Prompt

Structured prompting is a rapidly developing technique that enhances the performance and efficiency of large language models (LLMs) and other machine learning models by carefully crafting input prompts. Current research focuses on designing prompt structures for various applications, including debiasing LLMs, improving factual accuracy in text summarization, and adapting pre-trained models to low-resource tasks, often employing techniques like low-rank adaptation and hierarchical prompt designs. This approach offers significant advantages in parameter efficiency, reducing computational costs and improving performance, particularly in domains with limited data, thereby impacting fields like medical diagnosis and natural language processing.