{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/llm-detectaive-a-tool-for-fine-grained","title":"LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection","arxiv_id":"2408.04284","date":"2024-08-08","proceeding":null,"authors":["Mervat Abassy","Kareem Elozeiri","Alexander Aziz","Minh Ngoc Ta","Raj Vardhan Tomar","Bimarsha Adhikari","Saad El Dine Ahmed","Yuxia Wang","Osama Mohammed Afzal","Zhuohan Xie","Jonibek Mansurov","Ekaterina Artemova","Vladislav Mikhailov","Rui Xing","Jiahui Geng","Hasan Iqbal","Zain Muhammad Mujahid","Tarek Mahmoud","Akim Tsvigun","Alham Fikri Aji","Artem Shelmanov","Nizar Habash","Iryna Gurevych","Preslav Nakov"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2408.04284v2","url_pdf":"https://arxiv.org/pdf/2408.04284v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"llm-detectaive-a-tool-for-fine-grained","repo_url":"https://github.com/mbzuai-nlp/llm-detectaive","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"text-detection","task_name":"Text Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2408.04284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04284"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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