Principle Based Prompt

Principle-based prompting focuses on designing effective prompts—instructional text—to guide large language models (LLMs) and other AI models towards desired outputs. Current research explores optimizing prompt design through techniques like learning prompts from data (e.g., using mixture-of-experts architectures), generating prompts automatically from descriptions, and leveraging semantic information to create more effective prompts for specific tasks, such as image segmentation or event detection. This research aims to improve the efficiency and performance of AI systems by reducing the reliance on extensive fine-tuning and manual prompt engineering, impacting various applications from deepfake detection to multimodal fusion.