Instruction Data Selection

Instruction data selection focuses on optimizing the training data used to fine-tune large language models (LLMs), aiming to improve model performance and efficiency. Current research emphasizes methods that select high-quality and diverse instructions, often employing gradient-based analysis, clustering techniques, and ranking algorithms to identify the most influential examples. This research is crucial for reducing the computational cost of LLM training and improving the generalization and performance of models across various downstream tasks, impacting both the development of more efficient LLMs and their practical application in diverse fields.