Enhancing Large Language Model Performance through Self-Guided Data Selection for Instruction Tuning

A self-guided methodology for Large Language Models to autonomously identify and select high-quality data samples from open-source datasets, minimizing manual curation and optimizing resource utilization for instruction tuning.