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

• URL: http://arxiv.org/abs/2502.08281v1
• Date: Wed, 12 Feb 2025 10:38:22 GMT
• Title: Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
• Authors: Jipeng Qiang, Minjiang Huang, Yi Zhu, Yunhao Yuan, Chaowei Zhang, Kui Yu,
• Abstract summary: 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.
• Score: 21.727596753351072
• License:
• 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.

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