The Split and Rephrase task, which consists in splitting complex sentences into a sequence of shorter grammatical sentences, while preserving the original meaning, can facilitate the processing of complex texts for humans and machines alike. In this work, we describe an approach based on large language models, which improves over the state of the art by large margins on all the major metrics for the task, on publicly available datasets. We also describe results from two human evaluations that further establish the significant improvements obtained with large language models and the viability of the approach. We evaluate different strategies, including fine-tuning pretrained language models of varying parameter size, and applying both zero-shot and few-shot in-context learning on instruction-tuned language models. Although the latter were markedly outperformed by fine-tuned models, they still achieved promising results overall. Our results thus demonstrate the strong potential of different variants of large language models for the Split and Rephrase task, using relatively small amounts of training samples and model parameters overall.

翻译："分割与重述"任务旨在将复杂句子拆分为一系列语法正确的较短句子，同时保留原始语义，这有助于人类和机器处理复杂文本。本研究提出了一种基于大语言模型的方法，在公开数据集上，该方法在该任务的所有主要指标上均大幅超越现有技术水平。我们还报告了两项人工评估结果，进一步证实了大语言模型带来的显著改进及其方法的可行性。我们评估了多种策略，包括微调不同参数规模的预训练语言模型，以及在指令微调语言模型上应用零样本和少样本上下文学习。尽管后者显著逊色于微调模型，但其整体仍取得了令人满意的结果。因此，我们的研究结果证明，在使用相对较少的训练样本和模型参数的情况下，不同变体的大语言模型在"分割与重述"任务中均展现出强大潜力。