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New PlagiaCorpus dataset targets LLM-generated plagiarism detection

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PlagiaCorpus, a new bilingual dataset designed to detect plagiarism and text reuse, including instances generated by large language models (LLMs) and cross-lingual translations. The dataset comprises over 198,000 text records, featuring source passages from Wikipedia and book collections in both Arabic and English, alongside queries categorized by reuse difficulty. This resource aims to advance forensic plagiarism detection by providing a comprehensive benchmark for various forms of text manipulation. AI

IMPACT This dataset could improve the accuracy and robustness of plagiarism detection tools, particularly in identifying sophisticated reuse generated by AI.

RANK_REASON The item describes a new dataset released on arXiv for research purposes. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PlagiaCorpus dataset targets LLM-generated plagiarism detection

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The item describes a new dataset released on arXiv for research purposes. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Wadii Boulila ·

    PlagiaCorpus: A Bilingual, Adversarial, and Cross-Lingual Dataset for Forensic Plagiarism Detection

    PlagiaCorpus is a bilingual Arabic-English dataset for benchmarking plagiarism and text-reuse detection, including reuse produced by large language models (LLMs) and reuse across languages. It contains 198,643 text records: a source corpus of 34,885 passages drawn from English an…

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