# Sentence Simplification via Large Language Models

arXiv: https://arxiv.org/abs/2302.11957 · HTML: https://mlsanity.com/paper/2302.11957/

## 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 (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://mlsanity.com/paper/2005.00481/) (2020)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing](https://mlsanity.com/paper/1906.08104/) (2019)
- [Iterative Edit-Based Unsupervised Sentence Simplification](https://mlsanity.com/paper/2006.09639/) (2020)
- [Unsupervised Neural Text Simplification](https://mlsanity.com/paper/1810.07931/) (2018)
- [Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://mlsanity.com/paper/1810.11193/) (2018)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [Unsupervised Sentence Simplification Using Deep Semantics](https://mlsanity.com/paper/1507.08452/) (2015)
- [Chinese Lexical Simplification](https://mlsanity.com/paper/2010.07048/) (2020)

## Built on by (most cited first, 15 of 15)

- [LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model](https://mlsanity.com/paper/2310.04445/) (2023)
- [Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension](https://mlsanity.com/paper/2312.10126/) (2023)
- [Learning to Paraphrase Sentences to Different Complexity Levels](https://mlsanity.com/paper/2308.02226/) (2023)
- [Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers](https://mlsanity.com/paper/2311.06534/) (2023)
- [A New Dataset and Empirical Study for Sentence Simplification in Chinese](https://mlsanity.com/paper/2306.04188/) (2023)
- [Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide](https://mlsanity.com/paper/2503.07690/) (2025)
- [Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability](https://mlsanity.com/paper/2310.00355/) (2023)
- [Difficulty Estimation and Simplification of French Text Using LLMs](https://mlsanity.com/paper/2407.18061/) (2024)
- [MCTS: A Multi-Reference Chinese Text Simplification Dataset](https://mlsanity.com/paper/2306.02796/) (2023)
- [From Words to Workflows: Automating Business Processes](https://mlsanity.com/paper/2412.03446/) (2024)
- [Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss](https://mlsanity.com/paper/2505.16172/) (2025)
- [A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences](https://mlsanity.com/paper/2506.11681/) (2025)
- [Text Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cycles](https://mlsanity.com/paper/2509.11991/) (2025)
- [MuTSE: A Human-in-the-Loop Multi-use Text Simplification Evaluator](https://mlsanity.com/paper/2604.08947/) (2026)
- [Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting](https://mlsanity.com/paper/2610.03567/) (2026)

## 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.