Fast-DetectGPT

This code is for ICLR2024 long 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 | Demo | OpenReview

Brief Intro

Method	5-Model Generations ↑	ChatGPT/GPT-4 Generations ↑	Speedup ↑
DetectGPT	0.9554	0.7225	1x
Fast-DetectGPT	0.9887 (relative↑ 74.7%)	0.9338 (relative↑ 76.1%)	340x

The table shows detection accuracy (measured in AUROC) and computational speedup for machine-generated text detection. The white-box setting (directly using the source model) is used for detecting generations produced by five source models (5-model), whereas the black-box setting (utilizing surrogate models) targets ChatGPT and GPT-4 generations. AUROC results are averaged across various datasets and source models. Speedup assessments were conducted on a Tesla A100 GPU.

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

An example 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:

@misc{bao2023fast,
    url = {https://arxiv.org/abs/2310.05130},
    author = {Bao, Guangsheng and Zhao, Yanbin and Teng, Zhiyang and Yang, Linyi and Zhang, Yue},
    title = {Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature},
    publisher = {arXiv},
    year = {2023},
}