# How Large Language Models are Transforming Machine-Paraphrased Plagiarism

arXiv: https://arxiv.org/abs/2210.03568 · HTML: https://mlsanity.com/paper/2210.03568/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 54 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [With Little Power Comes Great Responsibility](https://mlsanity.com/paper/2010.06595/) (2020)
- [Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://mlsanity.com/paper/1610.03098/) (2016)
- [Paraphrasing with Large Language Models](https://mlsanity.com/paper/1911.09661/) (2019)
- [Neural Machine Translation with Reconstruction](https://mlsanity.com/paper/1611.01874/) (2016)
- [Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection](https://mlsanity.com/paper/2103.12450/) (2021)
- [Identifying Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2103.11909/) (2021)
- [Testing of Support Tools for Plagiarism Detection](https://mlsanity.com/paper/2002.04279/) (2020)
- [D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research](https://mlsanity.com/paper/2204.13384/) (2022)
- [Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1811.01116/) (2018)
- [Analyzing Non-Textual Content Elements to Detect Academic Plagiarism](https://mlsanity.com/paper/2106.05764/) (2021)

## Built on by (most cited first, 30 of 38)

- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing](https://mlsanity.com/paper/2305.16635/) (2023)
- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text](https://mlsanity.com/paper/2405.12689/) (2024)
- [Paraphrase Types Elicit Prompt Engineering Capabilities](https://mlsanity.com/paper/2406.19898/) (2024)
- [Paraphrase Detection: Human vs. Machine Content](https://mlsanity.com/paper/2303.13989/) (2023)
- [IMGTB: A Framework for Machine-Generated Text Detection Benchmarking](https://mlsanity.com/paper/2311.12574/) (2023)
- [Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better](https://mlsanity.com/paper/2402.00263/) (2024)
- [Improving Black-box Robustness with In-Context Rewriting](https://mlsanity.com/paper/2402.08225/) (2024)
- [Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency](https://mlsanity.com/paper/2304.11079/) (2023)
- [Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models](https://mlsanity.com/paper/2306.09308/) (2023)
- [mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection](https://mlsanity.com/paper/2506.01702/) (2025)
- [Reducing Sequence Length by Predicting Edit Operations with Large Language Models](https://mlsanity.com/paper/2305.11862/) (2023)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [Paraphrase Types for Generation and Detection](https://mlsanity.com/paper/2310.14863/) (2023)
- [KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2402.13671/) (2024)
- [Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features](https://mlsanity.com/paper/2502.17749/) (2025)
- [FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models](https://mlsanity.com/paper/2406.00839/) (2024)
- [PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection](https://mlsanity.com/paper/2406.16288/) (2024)
- [Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature](https://mlsanity.com/paper/2601.07533/) (2026)
- [Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models](https://mlsanity.com/paper/2605.08031/) (2026)
- [Towards Human Understanding of Paraphrase Types in Large Language Models](https://mlsanity.com/paper/2407.02302/) (2024)
- [Overview of the Plagiarism Detection Task at PAN 2025](https://mlsanity.com/paper/2510.06805/) (2025)
- [Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science](https://mlsanity.com/paper/2512.17752/) (2025)
- [Paraphrase Acquisition from Image Captions](https://mlsanity.com/paper/2301.11030/) (2023)
- [Task-Oriented Paraphrase Analytics](https://mlsanity.com/paper/2403.17564/) (2024)

## Abstract (arXiv)

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.