[论文解读] Testing of Detection Tools for AI-Generated Text

Debora Weber-Wulff, Alla Anohina-Naumeca+6 more|arXiv (Cornell University)|Jun 21, 2023

Artificial Intelligence in Healthcare and Education被引用 33

一句话总结

本论文评估了12种公开检测工具以及两种商业系统（Turnitin 和 PlagiarismCheck），并发现它们通常不准确且偏向将文本标注为人为撰写；混淆方法会显著降低检测性能。

ABSTRACT

Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for artificial intelligence generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

研究动机与目标

• 评估现有检测工具是否能够可靠地区分人为撰写的文本与AI生成的文本（ChatGPT时代的内容）。
• 评估翻译和内容混淆对检测性能的影响。
• 提供对当前检测工具及其在学术环境中的局限性的全面概述。

提出的方法

• 测试12种公开可用的检测工具和2种商业系统（Turnitin 和 PlagiarismCheck）。
• 使用原始文档集来评估准确性和错误类型。
• 进行准确性分析和错误类型分析，以识别偏差和失败模式。
• 检查机器翻译和内容混淆对检测结果的影响。

实验结果

研究问题

• RQ1 现有检测工具能否可靠地区分人为撰写的文本和AI生成的文本？
• RQ2 机器翻译技术是否会影响AI生成文本检测的准确性？
• RQ3 内容混淆技术会降低检测工具的性能吗？
• RQ4 检测器是否存在偏向将内容分类为人为撰写而非AI生成的偏见？

主要发现

• 检测工具既不准确也不可靠，无法区分AI生成文本与人工撰写文本。
• 主要偏向将输出分类为人为撰写而非AI生成。
• 内容混淆技术显著恶化检测器性能。
• 该研究提供了跨多种工具和设置的全面、方法学严谨的评估。
• 这一工作强调了在学术环境中使用检测工具的含义及局限性。