yaful/MAGE
收藏资源简介：
--- license: apache-2.0 --- <div align="center"> <h1>MAGE: Machine-generated Text Detection in the Wild</h1> </div> ## 🚀 Introduction Recent advances in large language models have enabled them to reach a level of text generation comparable to that of humans. These models show powerful capabilities across a wide range of content, including news article writing, story generation, and scientific writing. Such capability further narrows the gap between human-authored and machine-generated texts, highlighting the importance of machine-generated text detection to avoid potential risks such as fake news propagation and plagiarism. In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build **a comprehensive testbed for machine-generated text detection**, by gathering texts from various human writings and machine-generated texts generated by different LLMs. The data in this repository is used to evaluate the effectiveness of machine-generated detection methods, as described in our paper titled "MAGE: Machine-generated Text Detection in the Wild" (available at https://arxiv.org/abs/2305.13242). We invite you to test your own detection methods on our testbed and encourage you to star our [Github repo](https://github.com/yafuly/MAGE). ## 📚 Citation If you use this dataset in your research, please cite it as follows: ```bibtex @misc{li2024mage, title={MAGE: Machine-generated Text Detection in the Wild}, author={Yafu Li and Qintong Li and Leyang Cui and Wei Bi and Zhilin Wang and Longyue Wang and Linyi Yang and Shuming Shi and Yue Zhang}, year={2024}, eprint={2305.13242}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` We welcome contributions to improve this dataset! If you have any questions or feedback, please feel free to reach out at yafuly@gmail.com. <!-- # 🤝 Contributing -->
--- license: Apache-2.0 --- <div align="center"> <h1>MAGE：真实场景下的机器生成文本检测（Machine-generated Text Detection in the Wild）</h1> </div> ## 🚀 引言 近年来，大语言模型（Large Language Model）的技术进展使其文本生成能力已达到可与人类媲美的水平。这类模型在新闻撰稿、故事创作、学术写作等众多内容场景中均展现出强劲的生成能力。此类能力进一步缩小了人类创作文本与机器生成文本之间的差距，也凸显了机器生成文本检测技术的重要性，以规避虚假新闻传播、学术抄袭等潜在风险。在实际应用场景中，检测模型往往需要面对来自不同领域、由不同大语言模型生成的文本，且无法获知这些文本的具体来源。 为此，我们通过收集不同来源的人类创作文本与各类大语言模型生成的机器文本，构建了**一套面向机器生成文本检测的综合性测试基准平台**。本仓库收录的数据可用于评估机器生成文本检测方法的性能，相关细节已发表于题为《MAGE：真实场景下的机器生成文本检测》的论文（可在https://arxiv.org/abs/2305.13242获取）。我们诚挚邀请各位研究者在本测试基准平台上验证自研的检测方法，并欢迎为我们的[GitHub仓库](https://github.com/yafuly/MAGE)点亮Star。 ## 📚 引用规范 若您在研究中使用本数据集，请按以下格式引用： bibtex @misc{li2024mage, title={MAGE: Machine-generated Text Detection in the Wild}, author={Yafu Li and Qintong Li and Leyang Cui and Wei Bi and Zhilin Wang and Longyue Wang and Linyi Yang and Shuming Shi and Yue Zhang}, year={2024}, eprint={2305.13242}, archivePrefix={arXiv}, primaryClass={cs.CL} } 我们欢迎各类贡献以完善本数据集！若您有任何疑问或建议，可通过邮箱yafuly@gmail.com与我们联系。 <!-- # 🤝 贡献指南 -->
数据集概述
数据集名称
MAGE: Machine-generated Text Detection in the Wild
数据集目的
构建一个全面的测试平台，用于评估机器生成文本检测方法的有效性。数据集包含来自不同领域的人类写作和由不同大型语言模型生成的机器生成文本。
数据集内容
- 人类写作文本
- 机器生成文本（由不同大型语言模型生成）
数据集使用
用于评估和开发机器生成文本检测技术。
引用信息
bibtex @misc{li2024mage, title={MAGE: Machine-generated Text Detection in the Wild}, author={Yafu Li and Qintong Li and Leyang Cui and Wei Bi and Zhilin Wang and Longyue Wang and Linyi Yang and Shuming Shi and Yue Zhang}, year={2024}, eprint={2305.13242}, archivePrefix={arXiv}, primaryClass={cs.CL} }
