论文标题
语言模型如何改变机器参数的窃行为
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
论文作者
论文摘要
大型语言模型对文本生成的最新成功对学术完整性构成了严重威胁，因为pla窃者可以产生与原始作品无法区分的现实释义。然而，大型自回旋变压器在产生机器人的窃及其检测中的作用在文献中仍在发展。这项工作探讨了Arxiv，Student Thees和Wikipedia的科学文章的机器拼写液的T5和GPT-3。我们评估了六种自动解决方案和一项商业pla窃检测软件的检测性能，并与105名参与者进行有关检测性能和生成示例质量的人类研究。我们的结果表明，大型模型可以重写文本，很难将其识别为机器代表（53％的平均含量）。人类专家对GPT-3产生的释义质量的评价为原始文本（Clarity 4.0/5，Flumenty 4.2/5，相干3.8/5）。表现最佳的检测模型（GPT-3）在检测释义方面达到了66％的F1得分。
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.
