Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance. However, the fundamental problem of precisely defining and measuring data diversity remains underexplored, limiting clear guidance for data engineering. To address this, we systematically analyze 11 existing diversity measurement methods by evaluating their correlation with model performance through extensive fine-tuning experiments. Our results indicate that a reliable diversity measure should properly account for both inter-sample differences and the information distribution in the sample space. Building on this, we propose NovelSum, a new diversity metric based on sample-level "novelty." Experiments on both simulated and real-world data show that NovelSum accurately captures diversity variations and achieves a 0.97 correlation with instruction-tuned model performance, highlighting its value in guiding data engineering practices. With NovelSum as an optimization objective, we further develop a greedy, diversity-oriented data selection strategy that outperforms existing approaches, validating both the effectiveness and practical significance of our metric.

翻译：数据多样性对于大型语言模型的指令微调至关重要。现有研究探索了多种多样性感知的数据选择方法，以构建高质量数据集并提升模型性能。然而，如何精确定义和衡量数据多样性这一根本问题仍未得到充分探索，这限制了对数据工程实践的明确指导。为解决此问题，我们通过大量微调实验评估了11种现有多样性度量方法与模型性能的相关性，从而对其进行了系统性分析。我们的结果表明，一个可靠的多样性度量应恰当考虑样本间差异以及样本空间中的信息分布。基于此，我们提出了NovelSum，一种基于样本级"新颖性"的新多样性度量指标。在模拟数据和真实数据上的实验表明，NovelSum能够准确捕捉多样性变化，并与指令微调模型性能达到0.97的相关性，凸显了其在指导数据工程实践方面的价值。以NovelSum作为优化目标，我们进一步开发了一种贪婪的、面向多样性的数据选择策略，其性能优于现有方法，验证了我们所提度量指标的有效性与实际意义。