One-Shot Learning as Instruction Data Prospector for Large Language Models

Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce NUGGETS, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. NUGGETS assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. NUGGETS utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through comprehensive evaluations on two benchmarks, including MT-Bench and Alpaca-Eval, we show that instruction tuning with the top 1% of examples curated by NUGGETS substantially outperforms conventional methods employing the entire dataset.