Instruction Fine Tuning

Instruction fine-tuning (IFT) adapts pre-trained large language models (LLMs) to follow instructions more effectively, enhancing their performance on diverse downstream tasks. Current research focuses on improving the robustness and safety of IFT, addressing issues like data contamination and security vulnerabilities, while also exploring efficient methods like parameter-efficient fine-tuning and data selection strategies to reduce computational costs. This area is significant because it enables the development of more reliable and versatile LLMs for various applications, ranging from code generation and medical diagnosis to robotics and product information processing, while simultaneously mitigating potential risks associated with their deployment.