Sentence Simplification via Large Language Models

• DOI: 10.48550/arxiv.2302.11957
• Publication Date: 2023-01-01
• Authors: Feng, Yutao; Qiang, Jipeng; Li, Yun; Yuan, Yunhao; Zhu, Yi
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 01 natural sciences; Computation and Language (cs.CL); 0105 earth and related environmental sciences

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.