mlsanityData to 14 Oct 2026

Sentence Simplification via Large Language Models

arXiv 2302.11957 ↗First posted 23 Feb 2023cs.CL cs.AIcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 23 Feb 2023.

Builds on

18 of 36 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  7. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 548
  8. 2017Sentence Simplification with Deep Reinforcement Learningcited by 109
  9. 2020ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformationscited by 101
  10. 2018BLEU is Not Suitable for the Evaluation of Text Simplificationcited by 78
  11. 2019Controllable Sentence Simplificationcited by 78
  12. 2019EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editingcited by 63
6 more
  1. 2020Iterative Edit-Based Unsupervised Sentence Simplificationcited by 33
  2. 2018Unsupervised Neural Text Simplificationcited by 29
  3. 2018Integrating Transformer and Paraphrase Rules for Sentence Simplificationcited by 29
  4. 2021An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languagescited by 14
  5. 2015Unsupervised Sentence Simplification Using Deep Semanticscited by 9
  6. 2020Chinese Lexical Simplificationcited by 8

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Citing papers per year: 2023: 7; 2024: 2; 2025: 4; 2026: 2.

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  1. 2023LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Modelcited by 21
  2. 2023Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehensioncited by 16
  3. 2023Learning to Paraphrase Sentences to Different Complexity Levelscited by 11
  4. 2023Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizerscited by 8
  5. 2023A New Dataset and Empirical Study for Sentence Simplification in Chinesecited by 4
  6. 2025Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Dividecited by 4
  7. 2023Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readabilitycited by 1
  8. 2024Difficulty Estimation and Simplification of French Text Using LLMscited by 1
  9. 2023MCTS: A Multi-Reference Chinese Text Simplification Datasetcited by 0
  10. 2024From Words to Workflows: Automating Business Processescited by 0
  11. 2025Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Losscited by 0
  12. 2025A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentencescited by 0
3 more
  1. 2025Text Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cyclescited by 0
  2. 2026MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluatorcited by 0
  3. 2026Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spottingcited by 0