Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification

TL;DR

This paper comprehensively evaluates large language models across various text simplification tasks, demonstrating their superior performance over traditional methods and even human references, and discusses future directions in the field.

Contribution

It provides the first extensive analysis of LLMs in lexical, syntactic, sentence, and document simplification, comparing them with traditional approaches using both automatic and human evaluations.

Findings

LLMs outperform non-LLM methods in all four TS tasks.

LLMs often generate outputs surpassing human-annotated references.

The study offers insights into future directions for TS with LLMs.

Abstract

Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large language models (LLMs) have outperformed supervised non-LLM-based methods on sentence simplification. This study offers the first comprehensive analysis of LLM performance across four TS tasks: lexical, syntactic, sentence, and document simplification. We compare lightweight, closed-source and open-source LLMs against traditional non-LLM methods using automatic metrics and human evaluations. Our experiments reveal that LLMs not only outperform non-LLM approaches in all four tasks but also often generate outputs that exceed the quality of existing human-annotated references. Finally, we present some future directions of TS in the era of LLMs.