Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such texts in journalism, educational, and academic contexts have surfaced. SemEval 2024 introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection, aiming to develop automated systems for identifying machine-generated text and detecting potential misuse. In this paper, we i) propose a RoBERTa-BiLSTM based classifier designed to classify text into two categories: AI-generated or human ii) conduct a comparative study of our model with baseline approaches to evaluate its effectiveness. This paper contributes to the advancement of automatic text detection systems in addressing the challenges posed by machine-generated text misuse. Our architecture ranked 46th on the official leaderboard with an accuracy of 80.83 among 125.

翻译：大型语言模型（LLMs）在生成流畅回答以应对多样化用户查询方面展现了卓越能力。然而，此类文本在新闻、教育和学术领域可能被滥用的担忧日益凸显。SemEval 2024提出了"多生成器、多领域、多语言黑盒机器生成文本检测"任务，旨在开发自动化系统以识别机器生成文本并检测潜在滥用行为。本文中，我们：i）提出一种基于RoBERTa-BiLSTM的分类器，旨在将文本划分为AI生成或人类撰写两类；ii）通过对比实验将我们的模型与基线方法进行比较以评估其有效性。本研究通过应对机器生成文本滥用带来的挑战，推动了自动文本检测系统的发展。我们的架构在官方排行榜125个参赛系统中位列第46名，准确率达到80.83%。