# Testing of Support Tools for Plagiarism Detection

arXiv: https://arxiv.org/abs/2002.04279 · HTML: https://mlsanity.com/paper/2002.04279/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [How Large Language Models are Transforming Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2210.03568/) (2022)
- [Identifying Machine-Paraphrased Plagiarism](https://mlsanity.com/paper/2103.11909/) (2021)
- [Paraphrase Identification with Deep Learning: A Review of Datasets and Methods](https://mlsanity.com/paper/2212.06933/) (2022)
- [Corpus-Based Paraphrase Detection Experiments and Review](https://mlsanity.com/paper/2106.00145/) (2021)
- [Assisting Human Decisions in Document Matching](https://mlsanity.com/paper/2302.08450/) (2023)
- [STEREO: Scientific Text Reuse in Open Access Publications](https://mlsanity.com/paper/2112.11800/) (2021)
- [A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages](https://mlsanity.com/paper/2201.03423/) (2022)
- [Taxonomy of academic plagiarism methods](https://mlsanity.com/paper/2105.12068/) (2021)
- [Word Definitions from Large Language Models](https://mlsanity.com/paper/2311.06362/) (2023)
- [Responsible AI for Test Equity and Quality: The Duolingo English Test as a Case Study](https://mlsanity.com/paper/2409.07476/) (2024)

## Abstract (arXiv)

There is a general belief that software must be able to easily do things that humans find difficult. Since finding sources for plagiarism in a text is not an easy task, there is a wide-spread expectation that it must be simple for software to determine if a text is plagiarized or not. Software cannot determine plagiarism, but it can work as a support tool for identifying some text similarity that may constitute plagiarism. But how well do the various systems work? This paper reports on a collaborative test of 15 web-based text-matching systems that can be used when plagiarism is suspected. It was conducted by researchers from seven countries using test material in eight different languages, evaluating the effectiveness of the systems on single-source and multi-source documents. A usability examination was also performed. The sobering results show that although some systems can indeed help identify some plagiarized content, they clearly do not find all plagiarism and at times also identify non-plagiarized material as problematic.