Efficient Prompting

Efficient prompting aims to optimize the use of large language models (LLMs) by crafting concise and effective instructions, minimizing computational costs and maximizing performance. Current research focuses on developing automated prompt generation techniques, exploring efficient prompt structures (e.g., leveraging in-context learning and adaptive token merging), and adapting prompting strategies for various model architectures (including Vision Transformers and smaller LLMs). These advancements are crucial for deploying LLMs in resource-constrained environments like edge devices and for improving the efficiency and scalability of LLM-based applications across diverse tasks, including question answering and multilingual relation classification.