mlsanityData to 7 Oct 2026

Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection

arXiv 2103.12450 ↗First posted 23 Mar 2021cs.CL cs.AI cs.DLcited by 17

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Abstract (arXiv)

The rise of language models such as BERT allows for high-quality text paraphrasing. This is a problem to academic integrity, as it is difficult to differentiate between original and machine-generated content. We propose a benchmark consisting of paraphrased articles using recent language models relying on the Transformer architecture. Our contribution fosters future research of paraphrase detection systems as it offers a large collection of aligned original and paraphrased documents, a study regarding its structure, classification experiments with state-of-the-art systems, and we make our findings publicly available.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

11 of 20 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  7. 2020Longformer: The Long-Document Transformercited by 3,362
  8. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  9. 2021Transformers in Vision: A Surveycited by 1,009
  10. 2019Defending Against Neural Fake Newscited by 553
  11. 2021Identifying Machine-Paraphrased Plagiarismcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 2; 2024: 2; 2025: 3; 2026: 2.

17 papers.

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  1. 2025Voting or Consensus? Decision-Making in Multi-Agent Debatecited by 55
  2. 2022How Large Language Models are Transforming Machine-Paraphrased Plagiarismcited by 38
  3. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  4. 2022A Domain-adaptive Pre-training Approach for Language Bias Detection in Newscited by 16
  5. 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
  6. 2022Specialized Document Embeddings for Aspect-based Similarity of Research Paperscited by 12
  7. 2023Paraphrase Detection: Human vs. Machine Contentcited by 12
  8. 2021Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detectioncited by 10
  9. 2021Incorporating Word Sense Disambiguation in Neural Language Modelscited by 6
  10. 2023Paraphrase Types for Generation and Detectioncited by 4
  11. 2024PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detectioncited by 3
  12. 2025Overview of the Plagiarism Detection Task at PAN 2025cited by 2
5 more
  1. 2026L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)cited by 2
  2. 2022PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collectioncited by 1
  3. 2022MuLVE, A Multi-Language Vocabulary Evaluation Data Setcited by 0
  4. 2025Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Promptscited by 0
  5. 2026Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Rerankingcited by 0