SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
<|reference_start|>SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection: 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.<|reference_end|>
@article{wang2024semeval-2024,
    title={SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual
  Machine-Generated Text Detection},
    author={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},
    journal={Proceedings of the 18th International Workshop on Semantic
  Evaluation (SemEval-2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14183},
    primaryClass={cs.CL}
}