Sentence Simplification via Large Language Models
This addresses the problem of automating text simplification for accessibility, showing a significant advance over existing methods.
The study investigated whether large language models (LLMs) can serve as high-quality sentence simplification systems, finding that they outperform state-of-the-art methods and match human annotators in performance.
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.