Instruction Aware Prompt Tuning

Instruction-aware prompt tuning aims to improve the performance of large language models (LLMs) by efficiently adapting them to specific tasks through carefully crafted prompts that incorporate task instructions. Current research focuses on developing methods to generate these prompts automatically, often employing techniques like soft prompt generation with optimized architectures and activation functions, or symbolic program search for compile-time optimization. This approach offers a parameter-efficient alternative to full model fine-tuning, leading to improved performance and reduced computational costs across various tasks, including video summarization and retrieval-augmented generation.