Prompt Mixing

Prompt mixing is a technique used to combine multiple textual prompts to guide the generation of images or text, aiming to create outputs that capture the essence of several input descriptions. Current research focuses on developing efficient algorithms for prompt mixing, including adaptations of financial models like the Black-Scholes equation and methods leveraging large language models for prompt refinement and data augmentation. These techniques are proving valuable for enhancing data efficiency in training deep learning models, particularly for tasks with limited annotated data, and improving the performance of lightweight networks in applications like image generation and text classification.