Fast-DetectGPT

This code is for ICLR 2024 paper "Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature", where we borrow or extend some code from DetectGPT.

Paper | LocalDemo | OnlineDemo | OpenReview

Brief Intro

Environment

• Python3.8
• PyTorch1.10.0
• Setup the environment: bash setup.sh

(Notes: our experiments are run on 1 GPU of Tesla A100 with 80G memory.)

Local Demo

Please run following command locally for an interactive demo:

python scripts/local_infer.py

where the default reference and sampling models are both gpt-neo-2.7B.

We could use gpt-j-6B as the reference model to obtain more accurate detections:

python scripts/local_infer.py  --reference_model_name gpt-j-6B

An example (using gpt-j-6B as the reference model) looks like

Please enter your text: (Press Enter twice to start processing)
Disguised as police, they broke through a fence on Monday evening and broke into the cargo of a Swiss-bound plane to take the valuable items. The audacious heist occurred at an airport in a small European country, leaving authorities baffled and airline officials in shock.

Fast-DetectGPT criterion is 1.9299, suggesting that the text has a probability of 87% to be fake.

Workspace

Following folders are created for our experiments:

• ./exp_main -> experiments for 5-model generations (main.sh).
• ./exp_gpt3to4 -> experiments for GPT-3, ChatGPT, and GPT-4 generations (gpt3to4.sh).

(Notes: we share generations from GPT-3, ChatGPT, and GPT-4 in exp_gpt3to4/data for convenient reproduction.)

Citation

If you find this work useful, you can cite it with the following BibTex entry:

@inproceedings{bao2023fast,
  title={Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature},
  author={Bao, Guangsheng and Zhao, Yanbin and Teng, Zhiyang and Yang, Linyi and Zhang, Yue},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}