The Large Language Models (LLMs) exhibit remarkable ability to generate
fluent content across a wide spectrum of user queries. However, this capability
has raised concerns regarding misinformation and personal information leakage.
In this paper, we present our methods for the SemEval2024 Task8, aiming to
detect machine-generated text across various domains in both mono-lingual and
multi-lingual contexts. Our study comprehensively analyzes various methods to
detect machine-generated text, including statistical, neural, and pre-trained
model approaches. We also detail our experimental setup and perform a in-depth
error analysis to evaluate the effectiveness of these methods. Our methods
obtain an accuracy of 86.9\% on the test set of subtask-A mono and 83.7\% for
subtask-B. Furthermore, we also highlight the challenges and essential factors
for consideration in future studies.

本论文介绍了我们在 SemEval2024 Task8 中用于检测跨各个领域的机器生成文本的方法，包括统计、神经网络和预训练模型方法，并通过深入的错误分析评估了这些方法的有效性。在单语和多语境下，我们的方法在子任务 A 单语上获得 86.9％的准确率，在子任务 B 上获得 83.7％的准确率。此外，我们还强调了未来研究中的挑战和重要因素。