How Large Language Models are Transforming Machine-Paraphrase Plagiarism
Open accessAbstract
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 machineparaphrased 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.We make our code, data, and findings publicly available for research purposes.1 Original Text ... On April 29, 2017, Bill Gates partnered with Swiss tennis legend Roger Federer in playing the "Match for Africa" 4, a noncompetitive tennis match at a sold-out Key Arena in Seattle.The event was in support of Roger Federer Foundation's charity efforts in Africa.... Paraphrased using GPT-3 ... Bill Gates teamed up with Swiss tennis player Roger Federer to play in the "Match for Africa 4"
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Use of LLM-created text by students is unlikely to be spotted by existing text-matching software tools used by HEIs.
“current research suggests that use of the created text by students is unlikely to be spotted by existing text-matching software tools used by HEIs”
From Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond (2023) · cites this paper as Wahle, Ruas, Kirstein, et al., 2022
In Q5, the paper reports that PlagScan could not reliably identify machine-paraphrased text and that large language models made text matches harder to find. Its tests concern LLM-generated paraphrases and one commercial plagiarism-detection product, not all LLM-created text or the range of text-matching tools used by higher education institutions. (LLM, conf=0.86)
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- Authors
- Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B.
- Affiliations
- University of Göttingen (DE)
- Mercedes-Benz (Germany) (DE)
- Corresponding author
- Wahle, Jan Philip
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 952-963
- OpenAlex
- W4385572879
- Cited by
- 35
- References
- 40
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3 · 2023: 17
- FWCI
- 7.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- paraphrase generation, plagiarism detection, large language models, GPT-3, T5, academic integrity, scientific articles, student theses, Wikipedia
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