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Sentence Simplification via Large Language Models

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arxiv 2302.11957 v1 pith:V2D5Z37F submitted 2023-02-23 cs.CL cs.AI

Yutao Feng , Jipeng Qiang , Yun Li , Yunhao Yuan , Yi Zhu This is my paper · ORCID
classification cs.CLcs.AI
keywords llmssentencesimplificationlanguageabilitylargemodelssentences
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

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

  1. A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences

    cs.CL 2025-06 reject novelty 5.0 of 10

    A multi-agent GPT-4O pipeline with an internal semantic-lexical gate claims 70% success on simplifying 100 video game sentences, versus 48% for a single-agent version.

  2. Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Adding all missing named entities back into simplified biomedical text yields the highest cosine similarity and ROUGE-1 to the original among five insertion strategies, but the evaluation is partly circular and lacks ...

  3. A Practical Guide for Supporting Formative Assessment and Feedback Using Generative AI

    cs.CY 2025-05 conditional novelty 4.0 of 10

    A narrative review that aligns generative AI tools with formative assessment principles, provides classroom prompt examples, and identifies missing evaluation metrics for AI feedback.

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

    cs.CL 2025-02 conditional novelty 4.0 of 10

    In a four-task benchmark, GPT-4o, Llama3.1-70B, and Gemma2-2B outperform traditional text simplification systems on most automatic metrics, and GPT-4o is preferred over human-written references in a small human study.

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