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Aligning large language models(LLMs) with human is a critical step ineffectively utilizing their pre-trained capabilities across a wide array oflanguage tasks. Current instruction tuning practices often rely on expandingdataset size without a clear strategy for ensuring data quality, which caninadvertently introduce noise and degrade model performance. To address thischallenge, we introduce Nuggets, a novel and efficient methodology that employsone shot learning to select high-quality instruction data from expansivedatasets. Nuggets assesses the potential of individual instruction examples toact as effective one shot examples, thereby identifying those that cansignificantly enhance diverse task performance. Nuggets utilizes a scoringsystem based on the impact of candidate examples on the perplexity of a diverseanchor set, facilitating the selection of the most beneficial data forinstruction tuning. Through rigorous testing on two benchmarks, includingMT-Bench and Alpaca-Eval, we demonstrate that instruction tuning with the top1% of Nuggets-curated examples substantially outperforms conventional methodsthat use the full dataset. These findings advocate for a data selectionparadigm that prioritizes quality, offering a more efficient pathway to alignLLMs with humans.

One Shot Learning as Instruction Data Prospector for Large Language Models