神经语言模型是优秀的剽窃者吗？一个神经复述检测基准
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
AI总结：
针对BERT等语言模型生成高质量复述文本威胁学术诚信的问题，提出一个基于Transformer的复述检测基准，提供对齐文档集、结构研究及分类实验，促进未来检测系统研究。
摘要：
BERT等语言模型的兴起使得高质量文本复述成为可能。这给学术诚信带来了问题，因为很难区分原创内容与机器生成的内容。我们提出了一个基准，包含使用基于Transformer架构的近期语言模型生成的复述文章。我们的贡献促进了未来复述检测系统的研究，因为它提供了大量对齐的原创与复述文档、关于其结构的研究、使用state-of-the-art系统的分类实验，并且我们公开了研究结果。
英文摘要：
The rise of language models such as BERT allows for high-quality text paraphrasing. This is a problem to academic integrity, as it is difficult to differentiate between original and machine-generated content. We propose a benchmark consisting of paraphrased articles using recent language models relying on the Transformer architecture. Our contribution fosters future research of paraphrase detection systems as it offers a large collection of aligned original and paraphrased documents, a study regarding its structure, classification experiments with state-of-the-art systems, and we make our findings publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
