Official repository of paper "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection". Please star, watch, and fork our repo for the active updates!

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Yes, we propose the first Human vs. ChatGPT comparison corpus, named HC3.

我们提出了第一个 Human vs. ChatGPT 对比语料, 叫做 HC3.

The first version of the HC3 datasets are now available on 🤗 Huggingface Datasets:

• HC3-English
• HC3-Chinese

在中文社区，HC3 数据集也已在 ModelScope 上可用:

• HC3-English
• HC3-Chinese

Train/Test splits & filtered versions of the paper, ref to Google Drive links in HC3/README.md.

If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.

(Hosted on 🤗 Hugging Face Spaces)

We provide three kinds of detectors, all in Bilingual / 我们提供了三个版本的检测器，且都支持中英文:

• QA version / 问答版: detect whether an answer is generated by ChatGPT for certain question, using PLM-based classifiers / 判断某个问题的回答是否由ChatGPT生成，使用基于PTM的分类器来开发;
• Sinlge-text version / 独立文本版: detect whether a piece of text is ChatGPT generated, using PLM-based classifiers / 判断单条文本是否由ChatGPT生成，使用基于PTM的分类器来开发;
• Linguistic version / 语言学版: detect whether a piece of text is ChatGPT generated, using linguistic features / 判断单条文本是否由ChatGPT生成，使用基于语言学特征的模型来开发;

在 modelscope 中文社区平台，三个版本的检测器也都可用:

• QA version / 问答版
• Sinlge-text version / 独立文本版
• Linguistic version / 语言学版

The model weights are all available at 🤗 Hugging Face Models:

The English models are based on roberta-base. The Chinese models are based on hfl/chinese-roberta-wwm-ext.

Checkout this paper arxiv: 2301.07597

@article{guo-etal-2023-hc3,
    title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
    author = "Guo, Biyang  and
      Zhang, Xin  and
      Wang, Ziyuan  and
      Jiang, Minqi  and
      Nie, Jinran  and
      Ding, Yuxuan  and
      Yue, Jianwei  and
      Wu, Yupeng",
    journal={arXiv preprint arxiv:2301.07597}
    year = "2023",
}

On December 9, 2022, which is 10 days after the launch of ChatGPT, we started this project, for two purposes:

1. To create some open-source models for efficiently detecting ChatGPT-generated content;
2. To collect a valuable human-ChatGPT comparison Q&A corpus, to facilitate releated research.

2022 年 12 月 9 日，也就是 ChatGPT 推出的第 10 天，我们开始了这个项目，为了两个目的：

1. 做出一些开源模型工具来高效检测 ChatGPT 生成的内容；
2. 收集一批有价值的人类和 ChatGPT 对比的中英双语问答语料，来助力相关学术研究。

Welcome to follow our project! We have released a preview of our ChatGPT detectors, and the models, dataset will be open-sourced in about a week. We look forward to receiving feedback from the community to help improve the models and make contributions to open academic research together:)
欢迎关注我们项目，我们目前已经发布ChatGPT检测器预览版，并将于约一周内发布开源模型、数据集。期待得到广大群众的反馈，来帮助我们改进模型，为开放的学术研究一起做贡献！

We are a group of insignificant researchers (in the shadow of ChatGPT) hoping to do some significant work for the community. The team for this projects consists of PhD students and engineers from 6 universities/companies.
我们是一群（在 ChatGPT 的阴影下）渺小的研究人员，但希望为社区做一些有意义的事。这个项目的团队由来自6所大学/公司的博士生和工程师组成。

Biyang Guo	Minqi Jiang	Ziyuan Wang	Xin Zhang
Jinran Nie	Yuxuan Ding	Jianwei Yue	Yupeng Wu