Instruction mining is the automated process of evaluating and selecting high-quality instruction-response data from larger or uncurated datasets to fine-tune large language models. Rather than training models on massive volumes of unfiltered text, this technique uses quantitative criteria, linguistic indicators, or model-based difficulty scores to pinpoint the most effective and informative training samples. By filtering out low-quality, noisy, or redundant examples, instruction mining reduces computational costs and data curation effort while improving a language model's ability to accurately understand and follow diverse human instructions.
