Instruction tuning has achieved unprecedented success in NLP, turning large language models into versatile chatbots. However, the increasing variety and volume of instruction datasets demand significant computational resources. To address this, it is essential to extract a small and highly informative subset (i.e., Coreset) that achieves comparable performance to the full dataset. Achieving this goal poses non-trivial challenges: 1) data selection requires accurate data representations that reflect the training samples' quality, 2) considering the diverse nature of instruction datasets, and 3) ensuring the efficiency of the coreset selection algorithm for large models. To address these challenges, we propose Task-Agnostic Gradient Clustered COreset Selection (TAGCOS). Specifically, we leverage sample gradients as the data representations, perform clustering to group similar data, and apply an efficient greedy algorithm for coreset selection. Experimental results show that our algorithm, selecting only 5% of the data, surpasses other unsupervised methods and achieves performance close to that of the full dataset.

本研究解决了指令调整数据集逐渐增多所带来的计算资源需求问题，提出了一种新的核心集选择方法TAGCOS。该方法通过利用样本梯度作为数据表示，并结合聚类和贪心算法进行有效选择，实验结果显示仅选择5%的数据就能达到接近全数据集的性能，展示了其在大型模型中的应用潜力。

TAGCOS：面向任务无关的梯度聚类核心集选择用于指令调整数据