SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection (2404.14183v1)
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.
- Yuxia Wang (41 papers)
- Jonibek Mansurov (14 papers)
- Petar Ivanov (4 papers)
- Jinyan Su (20 papers)
- Artem Shelmanov (29 papers)
- Akim Tsvigun (12 papers)
- Osama Mohammed Afzal (9 papers)
- Tarek Mahmoud (7 papers)
- Giovanni Puccetti (12 papers)
- Thomas Arnold (13 papers)
- Chenxi Whitehouse (17 papers)
- Alham Fikri Aji (94 papers)
- Nizar Habash (66 papers)
- Iryna Gurevych (264 papers)
- Preslav Nakov (253 papers)