Prompt Fusion

Prompt fusion is a technique that enhances the performance of large language and vision-language models by combining information from multiple prompts or modalities. Current research focuses on developing efficient prompt fusion methods, often employing techniques like multi-space projection, cluster-based modality fusion, and transformer-based architectures to improve accuracy and reduce training time across diverse tasks such as image classification, 3D shape recognition, and scientific forecasting (e.g., inertial confinement fusion). This approach offers significant advantages in few-shot and zero-shot learning scenarios, particularly where data is scarce, and has the potential to improve the efficiency and generalizability of various AI applications.