Aldog12/MAGE

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license: apache-2.0
---
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<h1>MAGE: Machine-generated Text Detection in the Wild</h1>
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## 🚀 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 [e-mail].

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