# The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPT

arXiv: https://arxiv.org/abs/2505.12405 · HTML: https://mlsanity.com/paper/2505.12405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 18 May 2025.

## Builds on (most cited first, 12 of 12)

- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://mlsanity.com/paper/2101.08382/) (2021)
- [Paraphrase Generation with Latent Bag of Words](https://mlsanity.com/paper/2001.01941/) (2020)
- [Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text](https://mlsanity.com/paper/2405.12689/) (2024)
- [Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index](https://mlsanity.com/paper/2310.05030/) (2023)
- [A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts](https://mlsanity.com/paper/1912.12068/) (2019)
- [A Deep Network Model for Paraphrase Detection in Short Text Messages](https://mlsanity.com/paper/1712.02820/) (2017)
- [Corpus-Based Paraphrase Detection Experiments and Review](https://mlsanity.com/paper/2106.00145/) (2021)
- [Paraphrase Types for Generation and Detection](https://mlsanity.com/paper/2310.14863/) (2023)
- [ARPA: Armenian Paraphrase Detection Corpus and Models](https://mlsanity.com/paper/2009.12615/) (2020)
- [Multiple Genome Analytics Framework: The Case of All SARS-CoV-2 Complete Variants](https://mlsanity.com/paper/2201.05198/) (2022)

## Abstract (arXiv)

Generative AI paraphrased text can be used for copyright infringement and the AI paraphrased content can deprive substantial revenue from original content creators. Despite this recent surge of malicious use of generative AI, there are few academic publications that research this threat. In this article, we demonstrate the ability of pattern-based similarity detection for AI paraphrased news recognition. We propose an algorithmic scheme, which is not limited to detect whether an article is an AI paraphrase, but, more importantly, to identify that the source of infringement is the ChatGPT. The proposed method is tested with a benchmark dataset specifically created for this task that incorporates real articles from BBC, incorporating a total of 2,224 articles across five different news categories, as well as 2,224 paraphrased articles created with ChatGPT. Results show that our pattern similarity-based method, that makes no use of deep learning, can detect ChatGPT assisted paraphrased articles at percentages 96.23% for accuracy, 96.25% for precision, 96.21% for sensitivity, 96.25% for specificity and 96.23% for F1 score.