Abstract
The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diverse samples for textual data that uses principal components on the embedding vectors. The proposed approach is simple and compared with existing approaches using the new metric. We transform standard text classification benchmarks into benchmarks for ordered diverse sampling. Our empirical evaluation shows that prevailing approaches perform 6% to 61% worse than our method while also being more time inefficient. Ablation studies show how the parts of the new approach contribute to the overall metrics.
Abstract (translated)
多样性采样的目标是以最大化子集中包含的信息量的方式，选择具有代表性的数据子集，同时保持其基数较小。我们基于一个新的度量标准引入了有序多样本抽样问题，该度量标准用于衡量样本有序列表中的多样性。我们提出了一种新颖的方法，通过在嵌入向量上使用主成分分析来生成文本数据的有序多样化样本。所提出的这种方法是简单的，并且与现有方法相比，在新的度量标准下表现更优。我们将传统的文本分类基准转换为有序多样本抽样的基准测试。我们的实证评估表明，现有的方法比我们的方法性能低6%到61%，并且在时间效率上也较低效。消融研究表明了新方法的各个部分如何贡献于整体指标的表现。
URL
https://arxiv.org/abs/2503.10698
