The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to assess how different prompts, seed data selection strategies, filtering methods, or model settings affect the quality of paraphrased data (and downstream models). In this study, we investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. Using these incentive methods as part of instructions to LLMs augmenting text datasets, we measure their effects on generated texts lexical diversity and downstream model performance. We compare the effects over 5 different LLMs, 6 datasets and 2 downstream models. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.

翻译：最新的生成式大语言模型（LLMs）已应用于数据增强任务，即通过LLM对少量文本样本进行改写，并用于微调下游模型。然而，关于不同提示、种子数据选择策略、过滤方法或模型设置如何影响改写数据质量（及下游模型）的研究仍需深入。在本研究中，我们探究了众包中三种成熟的文本多样性激励方法：禁忌词、基于前期异常解的提示以及基于前期异常解的链式生成。通过将这些激励方法纳入LLM的文本数据集增强指令中，我们测量了它们对生成文本词汇多样性及下游模型性能的影响。我们对比了5种不同LLM、6个数据集及2种下游模型的效果。实验表明，禁忌词对多样性的提升最为显著，而提示方法则在下游模型性能上表现最优。