SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

• Main Authors: Wang, Yuxia, Mansurov, Jonibek, Ivanov, Petar, Su, Jinyan, Shelmanov, Artem, Tsvigun, Akim, Afzal, Osama Mohammed, Mahmoud, Tarek, Puccetti, Giovanni, Arnold, Thomas, Whitehouse, Chenxi, Aji, Alham Fikri, Habash, Nizar, Gurevych, Iryna, Nakov, Preslav
• Format: Journal Article
• Language: English
• Published: 22-04-2024
• Subjects: Computer Science - Computation and Language
• DOI: 10.48550/arxiv.2404.14183

Proceedings of the 18th International Workshop on Semantic
Evaluation (SemEval-2024) 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.