How Large Language Models are Transforming Machine-Paraphrase Plagiarism

Abstract

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"

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 35 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.