{"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/semeval-2024-task-8-multidomain-multimodel","title":"SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection","arxiv_id":"2404.14183","date":"2024-04-22","proceeding":null,"authors":["Yuxia Wang","Jonibek Mansurov","Petar Ivanov","Jinyan Su","Artem Shelmanov","Akim Tsvigun","Osama Mohammed Afzal","Tarek Mahmoud","Giovanni Puccetti","Thomas Arnold","Chenxi Whitehouse","Alham Fikri Aji","Nizar Habash","Iryna Gurevych","Preslav Nakov"],"abstract":"We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.","url_abs":"https://arxiv.org/abs/2404.14183v1","url_pdf":"https://arxiv.org/pdf/2404.14183v1.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":"semeval-2024-task-8-multidomain-multimodel","repo_url":"https://github.com/mbzuai-nlp/COLING-2025-Workshop-on-MGT-Detection-Task1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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":"https://syntology.ai/paper/2404.14183","atlas_url":"https://app.syntology.ai/?focus=2404.14183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14183"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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