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.
Abstract (translated)
我们展示了SemEval-2024 Task 8: 多生成器、多领域和多语言机器生成文本检测的主要结果和主要发现。该任务包括三个子任务。子任务A是一个二分类任务，确定文本是由人类编写还是由机器生成。这个子任务有两个路线：一个专注于英语文本的单语种路线和一个多语言路线。子任务B是检测文本的确切来源，分辨是是人类还是特定LLM生成。子任务C旨在确定文本中作者从人类到机器的转变点。该任务吸引了大量参与者：子任务A单语种（126），子任务A多语种（59），子任务B（70）和子任务C（30）。在本文中，我们介绍了该任务，分析了结果，并讨论了系统提交以及它们使用的技术。对于所有子任务，使用LLM的最佳系统。
URL
https://arxiv.org/abs/2404.14183
