  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.
