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.
@article{arxiv.2302.11957,
title = {Sentence Simplification via Large Language Models},
author = {Yutao Feng and Jipeng Qiang and Yun Li and Yunhao Yuan and Yi Zhu},
journal= {arXiv preprint arXiv:2302.11957},
year = {2023}
}