Overview of the Plagiarism Detection Task at PAN 2025
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914080451723264 |
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| author | Greiner-Petter, André Fröbe, Maik Wahle, Jan Philip Ruas, Terry Gipp, Bela Aizawa, Akiko Potthast, Martin |
| author_facet | Greiner-Petter, André Fröbe, Maik Wahle, Jan Philip Ruas, Terry Gipp, Bela Aizawa, Akiko Potthast, Martin |
| contents | The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective sources. We created a novel large-scale dataset of automatically generated plagiarism using three large language models: Llama, DeepSeek-R1, and Mistral. In this task overview paper, we outline the creation of this dataset, summarize and compare the results of all participants and four baselines, and evaluate the results on the last plagiarism detection task from PAN 2015 in order to interpret the robustness of the proposed approaches. We found that the current iteration does not invite a large variety of approaches as naive semantic similarity approaches based on embedding vectors provide promising results of up to 0.8 recall and 0.5 precision. In contrast, most of these approaches underperform significantly on the 2015 dataset, indicating a lack in generalizability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Overview of the Plagiarism Detection Task at PAN 2025 Greiner-Petter, André Fröbe, Maik Wahle, Jan Philip Ruas, Terry Gipp, Bela Aizawa, Akiko Potthast, Martin Computation and Language Information Retrieval The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective sources. We created a novel large-scale dataset of automatically generated plagiarism using three large language models: Llama, DeepSeek-R1, and Mistral. In this task overview paper, we outline the creation of this dataset, summarize and compare the results of all participants and four baselines, and evaluate the results on the last plagiarism detection task from PAN 2015 in order to interpret the robustness of the proposed approaches. We found that the current iteration does not invite a large variety of approaches as naive semantic similarity approaches based on embedding vectors provide promising results of up to 0.8 recall and 0.5 precision. In contrast, most of these approaches underperform significantly on the 2015 dataset, indicating a lack in generalizability. |
| title | Overview of the Plagiarism Detection Task at PAN 2025 |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2510.06805 |