To Write or Not to Write as a Machine? That’s the Question

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Robiert Sepúlveda-Torres;Iván Martínez-Murillo;Estela Saquete;Elena Lloret;Manuel Palomar
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Abstract

Considering the potential of tools such as ChatGPT or Gemini to generate texts in a similar way to a human would do, having reliable detectors of AI –AI-generated content (AIGC)– is vital to combat the misuse and the surrounding negative consequences of those tools. Most research on AIGC detection has focused on the English language, often overlooking other languages that also have tools capable of generating human-like texts, such is the case of the Spanish language. This paper proposes a novel multilingual and multi-task approach for detecting machine versus human-generated text. The first task classifies whether a text is written by a machine or by a human, which is the research objective of this paper. The second task consists in detect the language of the text. To evaluate the results of our approach, this study has framed the scope of the AuTexTification shared task and also we have collected a different dataset in Spanish. The experiments carried out in Spanish and English show that our approach is very competitive concerning the state of the art, as well as it can generalize better, thus being able to detect an AI-generated text in multiple domains.
像机器一样写作还是不写作?这就是问题所在
考虑到ChatGPT或Gemini等工具以类似于人类的方式生成文本的潜力,拥有可靠的人工智能检测器——人工智能生成的内容(AIGC)——对于打击这些工具的滥用和周围的负面后果至关重要。大多数关于AIGC检测的研究都集中在英语语言上,往往忽略了其他语言,这些语言也有能够生成类似人类文本的工具,比如西班牙语。本文提出了一种新的多语言、多任务的机器生成文本和人工生成文本检测方法。第一个任务是分类文本是由机器还是由人编写的,这是本文的研究目标。第二项任务是检测文本的语言。为了评估我们方法的结果,本研究确定了自动文本化共享任务的范围,并且我们还收集了西班牙语的不同数据集。在西班牙语和英语中进行的实验表明,我们的方法在目前的技术水平上非常有竞争力,而且它可以更好地泛化,从而能够在多个领域检测人工智能生成的文本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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