The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated. Thus, researchers have been working on developing effective systems to identify machine-generated text. In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence and explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text. We analyze the models' performance and address several research questions that rise during the analysis of the results. By conducting this research, we shed light on the capabilities and limitations of Artificial Intelligence generated text.
翻译:大型语言模型的普及和公开可用的ChatGPT标志着人工智能融入人们日常生活的重要转折点。学术界已经注意到这些技术进展并表达了对区分真实和人工生成的困难的担忧。因此,研究人员一直致力于开发有效的系统来识别机器生成的文本。在本研究中,我们利用GPT-3模型通过人工智能生成科学论文摘要,并探索了多种文本表示方法与机器学习模型的结合,以识别机器书写的文本。我们分析了模型的性能,并在分析结果的过程中解决了几个研究问题。通过进行这项研究,我们揭示了人工智能生成文本的能力和局限性。