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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.08281 |
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| _version_ | 1866917919896633344 |
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| author | Qiang, Jipeng Huang, Minjiang Zhu, Yi Yuan, Yunhao Zhang, Chaowei Yu, Kui |
| author_facet | Qiang, Jipeng Huang, Minjiang Zhu, Yi Yuan, Yunhao Zhang, Chaowei Yu, Kui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification Qiang, Jipeng Huang, Minjiang Zhu, Yi Yuan, Yunhao Zhang, Chaowei Yu, Kui Computation and Language 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. |
| title | Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.08281 |