LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
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arXiv
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| Natura: | Preprint |
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2024
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| author | Abassy, Mervat Elozeiri, Kareem Aziz, Alexander Ta, Minh Ngoc Tomar, Raj Vardhan Adhikari, Bimarsha Ahmed, Saad El Dine Wang, Yuxia Afzal, Osama Mohammed Xie, Zhuohan Mansurov, Jonibek Artemova, Ekaterina Mikhailov, Vladislav Xing, Rui Geng, Jiahui Iqbal, Hasan Mujahid, Zain Muhammad Mahmoud, Tarek Tsvigun, Akim Aji, Alham Fikri Shelmanov, Artem Habash, Nizar Gurevych, Iryna Nakov, Preslav |
| author_facet | Abassy, Mervat Elozeiri, Kareem Aziz, Alexander Ta, Minh Ngoc Tomar, Raj Vardhan Adhikari, Bimarsha Ahmed, Saad El Dine Wang, Yuxia Afzal, Osama Mohammed Xie, Zhuohan Mansurov, Jonibek Artemova, Ekaterina Mikhailov, Vladislav Xing, Rui Geng, Jiahui Iqbal, Hasan Mujahid, Zain Muhammad Mahmoud, Tarek Tsvigun, Akim Aji, Alham Fikri Shelmanov, Artem Habash, Nizar Gurevych, Iryna Nakov, Preslav |
| contents | The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains.
LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04284 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection Abassy, Mervat Elozeiri, Kareem Aziz, Alexander Ta, Minh Ngoc Tomar, Raj Vardhan Adhikari, Bimarsha Ahmed, Saad El Dine Wang, Yuxia Afzal, Osama Mohammed Xie, Zhuohan Mansurov, Jonibek Artemova, Ekaterina Mikhailov, Vladislav Xing, Rui Geng, Jiahui Iqbal, Hasan Mujahid, Zain Muhammad Mahmoud, Tarek Tsvigun, Akim Aji, Alham Fikri Shelmanov, Artem Habash, Nizar Gurevych, Iryna Nakov, Preslav Computation and Language The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains. LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c. |
| title | LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.04284 |