指令微调数据集的多样性度量与子集选择
Diversity Measurement and Subset Selection for Instruction Tuning Datasets
AI总结：
本文提出基于行列式点过程和对数行列式距离的多样性度量方法，用于指令微调数据集的子集选择，实验证明该度量与下游指令遵循性能相关，可指导数据选择策略。
摘要：
我们的目标是选择用于大型语言模型微调的数据子集，以更有效地遵循指令。先前的工作强调了数据集构建中多样性的重要性，但依赖于任务数量等启发式方法。在本文中，我们使用行列式点过程来捕捉指令微调数据集的多样性和质量，以进行子集选择。我们提出使用对数行列式距离来度量数据集多样性，该距离是目标数据集与最大多样性参考数据集之间的距离。我们的实验表明，在归一化权重梯度空间中提出的多样性度量与下游指令遵循性能相关。因此，它可用于告知数据选择何时最有帮助，并分析数据集构建策略。我们在各种指令微调数据集上展示了我们方法的实用性。
英文摘要：
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such as the number of tasks. In this paper, we use determinantal point processes to capture the diversity and quality of instruction tuning datasets for subset selection. We propose to measure dataset diversity with log determinant distance that is the distance between the dataset of interest and a maximally diverse reference dataset. Our experiments demonstrate that the proposed diversity measure in the normalized weight gradient space is correlated with downstream instruction-following performance. Consequently, it can be used to inform when data selection is the most helpful and to analyze dataset curation strategies. We demonstrate the utility of our approach on various instruction tuning datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
