Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning. Large Language models (LLMs) have demonstrated the ability to perform a variety of natural language processing tasks. However, it is not yet known whether LLMs can be served as a high-quality sentence simplification system. In this work, we empirically analyze the zero-/few-shot learning ability of LLMs by evaluating them on a number of benchmark test sets. Experimental results show LLMs outperform state-of-the-art sentence simplification methods, and are judged to be on a par with human annotators.

翻译：句子简化旨在将复杂句子重新表述为更简单的句子，同时保留原意。大语言模型（LLMs）已展现出执行多种自然语言处理任务的能力。然而，目前尚不清楚大语言模型能否作为高质量的句子简化系统。本研究通过在一系列基准测试集上评估大语言模型的零/少样本学习能力，进行了实证分析。实验结果表明，大语言模型的表现优于最先进的句子简化方法，且与人工标注者水平相当。