Adaptive Prompt

Adaptive prompting aims to optimize the input instructions (prompts) given to large language models (LLMs) and other deep learning models to improve performance on various downstream tasks. Current research focuses on developing methods to automatically generate or adapt prompts, often incorporating techniques like prompt tuning, dynamic prompt learning, and the integration of graph neural networks (GNNs) with LLMs. This research is significant because effective prompting can enhance model efficiency, generalization, and robustness, leading to improved performance in diverse applications such as image processing, drug discovery, and question answering.