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

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arxiv 2502.08281 v1 pith:3WPOBEU5 submitted 2025-02-12 cs.CL

Jipeng Qiang , Minjiang Huang , Yi Zhu , Yunhao Yuan , Chaowei Zhang , Kui Yu This is my paper
classification cs.CL
keywords llmssimplificationdocumentexistingfourlanguagelargelexical
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification

    cs.CL 2025-08 conditional novelty 6.0 of 10

    APIO automatically induces and optimizes instruction-list prompts for grammatical error correction and text simplification, reporting improved scores over prior prompt-based methods on BEA-2019 and ASSET.

  2. A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

    cs.CL 2026-07 accept novelty 5.0 of 10

    A human-in-the-loop corpus of scientific-summary simplifications with original, GPT-simplified, reader-annotated, and expert-edited versions for training and benchmarking simplification systems.

  3. Human--LLM Collaboration Is Transforming Complexity Metrics in Scientific Texts

    cs.CY 2026-06 unverdicted novelty 5.0 of 10

    Analysis of arXiv abstracts detects increased top-word turnover and flattening of LLM-style to complexity-metric relationships after 2022.

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