Instruction Data

Instruction data, comprising instruction-response pairs, is crucial for training large language models (LLMs) to effectively follow diverse instructions and align with human preferences. Current research focuses on efficiently generating high-quality instruction data, often leveraging existing LLMs or employing techniques like self-instruction and curriculum learning to improve data diversity and complexity, while also exploring methods for data filtering and selection to optimize model performance and reduce training costs. This field is vital for advancing LLM capabilities, enabling the development of more robust and reliable models for various applications, from code generation to multimodal tasks and specialized domains like finance and biomedicine.