# Paraphrasing with Large Language Models

arXiv: https://arxiv.org/abs/1911.09661 · HTML: https://mlsanity.com/paper/1911.09661/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 30 of 36)

- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning](https://mlsanity.com/paper/2208.08831/) (2022)
- [What is the Role of Small Models in the LLM Era: A Survey](https://mlsanity.com/paper/2409.06857/) (2024)
- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning](https://mlsanity.com/paper/2307.10234/) (2023)
- [Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning](https://mlsanity.com/paper/2202.00535/) (2022)
- [FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering](https://mlsanity.com/paper/2305.04329/) (2023)
- [LLM for SoC Security: A Paradigm Shift](https://mlsanity.com/paper/2310.06046/) (2023)
- [LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model](https://mlsanity.com/paper/2310.04445/) (2023)
- [Refocusing on Relevance: Personalization in NLG](https://mlsanity.com/paper/2109.05140/) (2021)
- [SHARE: a System for Hierarchical Assistive Recipe Editing](https://mlsanity.com/paper/2105.08185/) (2021)
- [Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs](https://mlsanity.com/paper/2407.04411/) (2024)
- [ConRPG: Paraphrase Generation using Contexts as Regularizer](https://mlsanity.com/paper/2109.00363/) (2021)
- [A Comprehensive Survey of Contamination Detection Methods in Large Language Models](https://mlsanity.com/paper/2404.00699/) (2024)
- [Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers](https://mlsanity.com/paper/2205.00177/) (2022)
- [An Approach to Improve Robustness of NLP Systems against ASR Errors](https://mlsanity.com/paper/2103.13610/) (2021)
- [Improving Black-box Robustness with In-Context Rewriting](https://mlsanity.com/paper/2402.08225/) (2024)
- [Math Word Problem Solving by Generating Linguistic Variants of Problem Statements](https://mlsanity.com/paper/2306.13899/) (2023)
- [Validating LLM-Generated Programs with Metamorphic Prompt Testing](https://mlsanity.com/paper/2406.06864/) (2024)
- [Uniform Complexity for Text Generation](https://mlsanity.com/paper/2204.05185/) (2022)
- [Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?](https://mlsanity.com/paper/2304.11065/) (2023)
- [Reconstruction of Differentially Private Text Sanitization via Large Language Models](https://mlsanity.com/paper/2410.12443/) (2024)
- [SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches](https://mlsanity.com/paper/2503.03703/) (2025)
- [Quick Starting Dialog Systems with Paraphrase Generation](https://mlsanity.com/paper/2204.02546/) (2022)
- [Understanding Metrics for Paraphrasing](https://mlsanity.com/paper/2205.13119/) (2022)
- [QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting](https://mlsanity.com/paper/2508.16697/) (2025)
- [TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs](https://mlsanity.com/paper/2605.24079/) (2026)
- [Paraphrase Generation as Unsupervised Machine Translation](https://mlsanity.com/paper/2109.02950/) (2021)

## Abstract (arXiv)

Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstream NLP tasks such as text classification, sentiment analysis and question answering with the aid of fine-tuning. We present a useful technique for using a large language model to perform the task of paraphrasing on a variety of texts and subjects. Our approach is demonstrated to be capable of generating paraphrases not only at a sentence level but also for longer spans of text such as paragraphs without needing to break the text into smaller chunks.