  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.
