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 the model. 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 and
6 datasets. We show that diversity is most increased by taboo words, while
downstream model performance is highest when previously created paraphrases are
used as hints.

最新的生成型大规模语言模型（LLM）被应用于数据增强任务，在这些任务中使用少量文本样本进行 LLM 重述，然后用于模型的微调。本研究调查了三种在众包中广泛使用的文本多样性激励方法：禁忌词、先前异常解决方案的提示和先前异常解决方案的链接，并使用它们作为指导 LLM 对文本数据集进行增强的一部分的指令，测量它们对生成文本的词汇多样性和下游模型性能的影响。我们比较了在 5 种不同 LLM 和 6 个数据集上的影响效果。研究结果表明，禁忌词对多样性的增加最为显著，而使用先前创建的重述作为提示时下游模型性能最佳。